Lorica

lorica

Exploring Graph RAG: Enhancing Data Access and Evaluation Techniques

AI and RAG in Knowledge Graph Enlightenment 

The future of AI chips is about more than NVIDIA: AMD, Intel, chiplets, upstarts, analog AI, optical computing, and AI chips designed by AI. The interest and investment in AI is skyrocketing, and generative AI is fueling it. Over one-third of CxOs have already embraced GenAI in their operations, with nearly half preparing to invest in it. reportedly What’s powering it all – AI chips – used to receive less attention. Up to the moment, OpenAI’s for a “wildly-ambitious” tech project to boost the world’s chip capacity. Sam Altman claimed he wants to raise up to $7 trillion and sensationalism aside, however, keeping an eye on AI chips means being aware of today’s blockers and tomorrow’s opportunities. Geopolitics According to a , the global AI chip market is expected to reach $89.6 Billion by 2029. The demand for AI chips has increased substantially over time. Growth in AI technology, rising demand for AI chips in consumer electronics, and AI chip innovation all contribute to this forecast. recent study by IMARC Few people have more insights to share on AI hardware than . In an extensive conversation, Pialis shared his insider’s perspective on the AI chip landscape, the transformative rise of chiplets, specialized hardware for training and inference, the future of AI chips and emerging directions like analog and optical computing, and much more. Tony Pialis, CEO and co-founder of Alphawave An AI Leader Behind the Scenes Pialis is a semiconductor serial entrepreneur. He has previously founded and successfully sold two startups: Snowbush Microelectronics and V Semiconductor Inc. V Semiconductor was acquired by Intel in 2012, where Pialis spent a few years as the Vice President of Analog and Mixed-Signal IP. Pialis co-founded AlphaWave in 2017 with the goal of becoming “the next great semiconductor company.” Alphawave went public in 2021 with an impressive market cap of $4.5 billion. The company’s core products include silicon IP, chips, custom silicon, and high-speed interconnect technology tailored for major hyper-scaler customers like Google, Microsoft, Amazon, and Meta. Alphawave is an AI leader behind the scenes, powering hyper scalers and founded on a couple of key premises that Pialis laid out. https://pod.co/orchestrate-all-the-things-podcast-connecting-the-dots-with-george-anadiotis/the-future-of-ai-hardware-leaders-dark-horses-and-rising-stars-featuring-tony-pialis-alphawave-ceo-co-founder?embedable=true , the major players in terms of scaling out data centers and computing these days are no longer the Ciscos of the world. It’s transitioned to Hyperscalers – the Googles, Microsofts, Amazons, and Metas. Hyperscalers have internal design capability, and they also build their own servers, networks, data centers, and campuses. First , the major challenge facing AI moving forward is not computing. Pialis’ view is that the ability to design and implement compute is there. The challenge is the connectivity needed to connect the compute in order to process all of the data. This is what AlphaWave is focusing on. Second The Explosion of Specialized AI Hardware While consumer apps like ChatGPT drove the initial hype cycle in 2023, enterprise adoption reports are mixed. However, according to Pialis, the AI semiconductor sector has seen an enormous explosion of investment and new in the second half of 2023 across industries and geographies. design starts Pialis cited major national initiatives around building domestic AI chip capabilities in the US, Canada, UK, France, Germany, Korea, Taiwan and Japan. After years of relying largely on NVIDIA, governments want to foster their own chip industries to reduce strategic dependence on any one vendor. While NVIDIA CEO Jensen Huang also says that , it does not seem like this also includes the hardware layer. every country needs Sovereign AI Pialis believes this surging demand is spurring not just startups but also tech giants to develop specialized training and inference hardware. In his view, not every organization could or should develop its own AI model, but that’s bound to change. “Over time, AI will inevitably progress to be something like a utility, where hyperscalers will provide access to all of the compute power, like electricity. I think it will be reasonably priced. Anyone will be able to use this utility to train, develop, optimize and deploy their own models. But there’s going to be a lot of profit taking between now and then, and who knows how long it’ll take to eventually reach that state”, said Pialis. Leaders, Dark Horses, and Rising Stars in the AI Chip Race Few people would disagree that NVIDIA is clearly the dominant force in AI accelerators today, and Pialis shares this view. He sees AMD as a strong second place and praised CEO Lisa Su for her leadership. Others such as Ben Lorica also see . favorable winds for AMD in the GenAI chip market But Pialis cautioned not to underestimate Intel, calling it a dark horse that could still prove highly formidable in this market. More specifically, Pialis spoke highly of . Intel’s Habana acqui-hired division led by David Dahan Having worked at Intel, Pialis has an insider view. Having , and kept track of their , we tend to agree. Dahan helped architect new Intel processors that have showcased performance exceeding NVIDIA’s latest GPUs on key benchmarks. covered Habana, met Dahan MLPerf results But performance is not everything. As Pialis also noted, NVIDIA’s software platform including CUDA gives it a huge advantage in the AI chip segment. The ecosystem effects are strong, as engineers and researchers develop optimized frameworks and models for NVIDIA’s architecture. However, there is potential for alternatives; for example, . Pialis opined that a rule of thumb for AI hardware companies is that for every hardware engineer, they need 2-3 software engineers. So even though there’s a lot of talk about NVIDIA and hardware, the vast majority of the investment actually goes into the software. building upon AMD’s hardware and software stack https://www.youtube.com/watch?v=VYx1rGieDmE&embedable=true This was confirmed by NVIDIA’s Dave Salvator. At the , Salvator said that NVIDIA has twice as many software engineers as hardware engineers. That’s not at all by accident, as he emphasized. last MLPerf results briefing for 2023 However, Pialis sees far more potential for challengers in . The reason is that standards are still emerging in that area. As OctoML’s Luis Ceze , there’s for LLM serving, for portable deployment, and and for multiplexed fined-tuned model inference. Inference, is in fact, a bigger market than training as Pialis noted. the inference accelerator market shared vLLM MLC-LLM LoRAX Punica “There’s so much focus on training, large models and the cost of training. But it’s deployment where we all benefit from, on the inference side. That needs significant scale. That needs a different solution. More chips will inevitably be sold on the inference side than the training side. I’m sure business plans also improve with that increase in volume”, Pialis said. Startups like and are attracting significant funding, and so are companies from countries like the UK, Korea, and China trying to reduce reliance on US firms. As far as hyperscalers go, Pialis sees Amazon and Google as being neck to neck, with Microsoft showing strong momentum, and Meta being behind and rumored to acquire one of the smaller startups. Groq Tenstorrent Join the Orchestrate All the Things Newsletter Stories about how Technology, Data, AI, and Media flow into each other shaping our lives. Analysis, Essays, Interviews, and News. Mid-to-long form, 1-3 times per month. The Chiplet Technological Revolution One of the biggest transformations underway in semiconductors, according to Pialis, is the shift to . Previously, progress meant integrating more and more functionality into a single monolithic chip. But transistor dimensions are now down to ~5 atoms wide, and even tiny defects can ruin a whole chip. chiplets Pialis shared an anecdote from his own experience to highlight this. As he related, when he visited OpenAI at some point, he saw a bunch of engineers on their knees praying in front of servers. Not because they were “ ”, but rather because they didn’t want the model they were training to crash. feeling the AGI Some of us may have first heard about chiplets in the context of the China–US trade war that spills over to technology. Chiplets are a key element in the strategy of both and the . China US For Pialis, “chiplets are another technological revolution to deal with the problem that God has thrown at us.” That may sound like a wild appeal to the supernatural, but it has more to do with the challenges of physics at nanoscale. “When we build transistors, which are the basic building blocks of any integrated circuit, we’re stacking atoms. When you’re stacking atoms, the laws of probability, the laws of averaging, fall apart, because now it’s two atoms rather than hundreds of atoms and electrons. So what you get is defects”, Pialis explained. Chiplets are an innovative solution to this nanoscale physics challenge. Rather than one giant chip, designs are split into smaller Lego-like chiplets that can be connected through advanced packaging. This modularity lets chipmakers avoid throwing out entire devices due to one small flaw. The benefits, Pialis said, are important for both makers and buyers. “Silicon is no longer the foundation of leading edge semiconductors; it’s the package. Silicon is now something that is a component on this packaging. There’s been a lot of talk about semiconductor supply chains. There’s a lot of silicon capacity now. Where there’s still very little to no capacity is on the packaging, specifically for designs built using chiplets”, Pialis noted. Chiplets as Lego Building Blocks However, there is a standout example of an AI hardware company that seems to be successful, despite going the opposite way: Cerebras. Cerebras is known for its wafer-scale hardware. Pialis was positive that Cerebras was going to encounter the same defects as everyone else. The difference with Cerebras is they have redundancy. A wafer is like a panel, Pialis noted. Cerebras doesn’t even cut the panel. What they do is they have software that works around the defects. Fundamentally, they’re using silicon as the foundation rather than packaging. They have multiple chips that are arrayed, but they’re not cut. They just connect on the wafer. It definitely is a different approach. However, Pialis added, there are advantages to cutting things up. Imagine a vendor like Intel is trying to build different units such as a CPU, a GPU, a DPU, or a networking device. By breaking things up into smaller pieces, they become like Lego building blocks. So you can have a processor core chiplet, a PCI Express connectivity chiplet, an Ethernet networking chiplet, a DDR memory I/O chiplet, and a memory I/O chiplet. These chiplets can be mixed and matched in a package to build out an entire product portfolio. Pialis thinks that from a design complexity and upfront investment perspective, that’s a winning formula. Pialis sees massive benefits in chiplets, reducing costs by over 60% and power consumption by 40%. This is a major incentive for hyperscale data centers. While he sees Apple, AMD, and Intel leading in chiplets today, Pialis believes chiplets will soon become essential for any company focused on leading-edge hardware. Join the Orchestrate All the Things Newsletter Stories about how Technology, Data, AI, and Media flow into each other shaping our lives.Analysis, Essays, Interviews, and News. Mid-to-long form, 1-3 times per month. Software and Chiplet Modularity, Composability, Programmability Drawing parallels from the world of software engineering, one can wonder how come the modularity that chiplets bring to the table did not prevail earlier. But in hardware, up to now, the winners were the ones that could integrate into a monolithic device the most. The reason is that this drove down cost, which Pialis noted has created “a maniacal focus on integration.” However, as we now approach the dimensions of a single atom, the manufacturing cost eclipses the cost of integration. But even in software, . excessive modularity can also incur excessive overhead Pialis expects that some hardware vendors may take the chiplet approach too far. If functionality is broken into too many tiny pieces, the cost of integrating all of these becomes limiting. So ultimately, it’ll be a hybrid approach that wins. There are two ways of splitting things up with chiplets. The first way of building chiplets is building a single Lego block, mirroring it over and over with the same functionality, and they just talk to each other. That seems more akin to the “traditional” approach. However, one may wonder how these identical chiplet building blocks can be synthesized. The answer is, again, software. Different packages can be composed for different needs, based on the same chiplet, using the software. For example, multiples of 1, 2, 4, or 8 chiplets. The same silicon, just packaged differently, priced differently, and with different software to leverage the incremental compute and memory bandwidth associated with these devices. The other way of building chiplets is by splitting and cutting, creating Lego blocks for different types of functionality. This creates chiplets like a compute Lego block, a training I/O Lego block, a network I/O Lego block, and so on. Pialis sees more momentum behind this approach, as reusable Lego blocks can accelerate other products too. Analog AI, Optical Computing, and AI-Assisted Hardware Design Chiplets may be the immediate way forward. But at a time when power-hungry digital accelerators like GPUs dominate, Pialis claims there’s more to the future of AI chips. And that involves a radical break with the current paradigm. AI needs massively parallelized arithmetic processing, and binary is the dominant approach for this. Using binary compute, numbers are represented as ones and zeros, and floating point arithmetic is done defining precision and range. In analog arithmetic processing, floating point can be represented via voltages or currents. In theory, that may have infinite precision. In the real world where noise exists, that may yield less precision. But that may well work for applications. Using very small currents keeps things operating at low power. Edge AI https://pod.co/orchestrate-all-the-things-podcast-connecting-the-dots-with-george-anadiotis/the-future-of-ai-hardware-leaders-dark-horses-and-rising-stars-featuring-tony-pialis-alphawave-ceo-co-founder?embedable=true There’s another form of compute that some companies are investing in: optical compute for arithmetic. Optical compute uses the properties of optics to implement what are called (multiply & accumulate functionality) which is the core of any arithmetic unit. This results in even lower power demands. MACs Pialis noted that analog and optical approaches are attracting billions in investment to meet specialized needs around computational scale, energy efficiency, and precision. However, it remains unclear if analog techniques can scale effectively to match digital compute for cutting-edge AI models. There is active debate on this question in the hardware community. Even though these techniques are often dismissed as impractical, Pialis believes the investment will eventually pay off. For example, recently, it was reported that capable of processing computer vision tasks with remarkable speed and energy efficiency. researchers at Tsinghua University have developed the world’s first super all-analog photoelectric chip Finally, there’s also the question of . Pialis said that the most effective hardware designers these days are the best software developers. But if AI models can be trained by leveraging the wealth of their experience, that may bring about a sea change. using AI to help design hardware to power AI While the future of AI chips is unknown, Pialis believes the fundamental principles of engineering are timeless. Hopefully, that translates to something that does not end up draining the world’s energy and resources on the promise of future gains to be made.

What Is the Future of AI Chips? Leaders, Dark Horses, and Rising Stars

Graphs, analytics, and Generative AI. An account of the different ways graphs and AI mingle, plus industry and research news. Is a generative AI preamble necessary for a newsletter focused on , , , and ? Normally, it should not be. However, the influence of generative AI on the items included in this issue was overwhelming. There is a simple explanation for that. Knowledge Graphs Graph Databases Graph Analytics Graph AI It’s been a year since Generative AI burst into the mainstream with the release of ChatGPT. Notwithstanding a rather spotty record both in terms of technical performance and accuracy as well as in terms of business reliability, there’s no denying that Generative AI has captured the attention of executives worldwide. Mentions of “generative AI” on earnings calls have skyrocketed since ChatGPT made its debut, rising from 28 in Q4’22 to 2,081 in Q3’23 — a 74x increase. With the majority of companies being at an early stage in their AI journey, executives feel the pressure to act amid the generative AI rush. What this means is that . Adoption is expected to grow 36% YoY in 2030 according to Forrester, and more than 100M people in the US alone will use generative AI next year. Therefore, vendors are looking to position their products accordingly. generative AI has tremendous mindshare Played right, this can be more than a marketing scheme. There are ways in which graphs and generative AI can complement each other, leading to . responsible enterprise decisions with knowledge-enriched Generative AI Let’s take a brief tour of product offerings and research efforts in that direction unveiled in the last few months. Vectors, graphs, and RAG RAG stands for Retrieval Augmented Generation. It’s a technique through which Large Language Models like ChatGPT can be instructed to use specific background knowledge to contextualize their processing. This offers a conversational interface to proprietary data, making LLMs applicable to business scenarios that require this. RAG is the main reason why the interest in vector databases has skyrocketed in the last year. Like all machine learning models, LLMs work with vectors. Therefore, having a vector database to store information and feed it to LLMs seems like a reasonable choice for RAG. However, it’s not the only one. In fact, as people like Damien Benveniste argue, . Graph Databases may be a better choice for RAG Using graphs, the relationships between the different entities in the text can be extracted and used to construct a knowledge base of the information contained within the text. An LLM is good at extracting that kind of triplet information: [ENTITY A] -> [RELATIONSHIP] -> [ENTITY B] Once the information is parsed, it can be stored in a graph database. The information stored is the knowledge base, not the original text. For information retrieval, the LLM needs to come up with an Entity query related to the question to retrieve the related entities and relationships. The retrieved information is much more concise and to the point than in the case of vector databases. Tony Seale, on his part, argues that vectors need graphs. Embedding vectors are a pivotal tool when using Generative AI. While vectors might initially seem an unlikely partner to graphs, their relationship is more intricate than it first appears. And Ben Lorica makes the case for . boosting LLMs with external knowledge using Knowledge Graphs Graph databases riding the RAG wave RAG is an alternative to fine-tuning LLMs, which seems less demanding and more immediately applicable. What’s more, it can be offered by vendors whose core competency is data management. Since they already have a footprint in organizations, data management vendors can do the heavy lifting of integrating with LLMs. It’s a win-win: executives get to check their gen AI boxes, and vendors get to expand their offering, catching the buzz and keeping clients happy. This is the reason why we’re seeing graph database vendors adding vector capabilities to their products. in its offering in August 2023. The idea is to combine the best of both worlds as part of the . Neo4j was the first one to include vector capabilities roadmap newly appointed Neo4j CPO Sudhir Hasbe announced in July 2023 Neo4j followed that up by . Amazon Neptune followed suit, recently announcing vector search as part of its Neptune Analytics engine; see below. announcing a collaboration with AWS Graph analytics meets HPC Neo4j and Amazon Neptune seem to have been on similar trajectories. Another thing that both vendors unveiled in the last few months is new analytics engines, leveraging parallelism to achieve speedup in processing in graph analytics scenarios previously underserved. As , for Neo4j’s new analytics engine typical examples are analytical queries that traverse most of the graph data. The parallel runtime is specifically designed to address these analytical queries. Hasbe shared The is geared towards three use cases. First, ephemeral analytics. These are workflows where customers just need to spin up a graph really quickly, run some analysis, and turn it off. new Neptune Analytics engine Second, low-latency analytical queries. That involves established machine learning pipelines with feature tables to perform real-time predictions. The third use case is building GenAI applications. Being able to perform vector similarity search when storing embeddings on Neptune analytics means it’s much easier to translate natural language questions into graph queries. To power their new analytics engines, both teams seem to have borrowed a page from high-performance compute (HPC). Neo4j’s implementation was directly inspired by the research paper “Morsel-Driven Parallelism: A NUMA-Aware Query Evaluation Framework for the Many-Core Age.” While we don’t have specific pointers for Amazon Neptune’s implementation, Amazon Neptune General Manager Brad Bebee acknowledged certain similarities. Both teams see a broad subset of graph customers and graph use cases. And both teams have members who are familiar with the literature and techniques coming from (HPC) processing of large-scale graphs. Parallel processing and memory optimization techniques are things that are well-understood in the HPC community. The difference is that HPC researchers are often solving a very specific graph problem on a very specific graph. For a service that has to solve graph problems for many customers, the main challenge is to generalize the techniques that can work well for HPC. Graph database entries and exits Even though Neo4j and Amazon are well-established in the graph database space, there is lots of action in this space beyond these two. Most vendors are working on positioning themselves in the generative AI landscape and/or adding conversational interfaces to their products. But that does not mean that generative AI is all there is out there – there are other use cases as well. , aiming to tackle complex problems at scale. Aerospike Graph is a new entry in the graph database market Aerospike started out as a key-value store. Eventually, the initial offering expanded to include the document model (JSON) as well as a SQL interface via Starburst. Graph was the next step, with adoption being reportedly customer-driven. A team was built for Aerospike Graph, including Apache TinkerPop founder Marko Rodriguez and key contributors to the project. They helped Aerospike create a graph layer that interfaces with the core engine in a way that scales out horizontally in a shared-nothing architecture. Coincidentally, it was only a few days after Aerospike Graph’s official unveiling that another database vendor with a similar profile who had recently entered the graph database market announced its exit. In 2019, , citing similar reasoning: they wanted to offer performance and scalability. In 2023, : Redis introduced its graph database they wound down RedisGraph, saying “Many analyst reports predicted that graph databases would grow exponentially. However, based on our experience, companies often need help to develop software based on graph databases. It requires a lot of new technical skills, such as graph data modeling, query composition, and query optimization. As with any technology, graph databases have their limitations and disadvantages. This learning curve is steep. Proof-of-concepts can take much longer than predicted, and the success rate can be low relative to other database models. For customers and their development teams, this often means frustration. For database vendors like Redis, this means that the total pre-sales (as well as post-sales) investment is very high relative to other database models”. Scoping and building knowledge graphs Building knowledge graphs is supposedly a huge and terrifying project, as . But that perception is mostly coming from software engineers who think that a knowledge graph has to be enormous. Mike Dillinger notes Tech organizations are overflowing with engineering managers who think (or expect) that engineers can do anything and everything. And there are precious few product managers who are familiar enough with knowledge graphs to frame knowledge graph building in terms that managers and engineers can grok and buy into. The end result is that organizations don’t implement crucial but unfamiliar tech, like knowledge graphs. Contrary to widespread assumptions, , creating and curating knowledge graphs is not an inherently manual process, even if quality management requires expert review. He shares some of the latest research efforts for assisted knowledge graph creation. Dillinger adds The topic of assisted knowledge graph creation has received renewed attention in the last few months, again owing to the generative AI momentum. There are good reasons for this. LLMs can assist in , as Rahul Nayak goes to show. converting any text into a graph of concepts One such effort that has drawn lots of attention is . MechGPT first breaks down texts into small chunks. Each chunk is fed to a general-purpose LLM that generates question-answer pairs summarizing key concepts. MechGPT, a language model specifically fine-tuned for constructing knowledge graphs Bonus track 1: Yejin Choi in conversation with Bill Gates, . Α combination of LLMs (magic but opaque/subsymbolic) with knowledge graphs (explicit/symbolic) is the path forward. summarized by Jesús Barrasa Bonus track 2: by Peter Lawrence. Prompting an LLM with an ontology to drive Knowledge Graph extraction from unstructured documents Ontology modeling for Knowledge Graphs with SHACL What is an ontology? That may seem like a trivial question for people familiar with knowledge graphs, but as , it is not. Cagle defines an ontology as a set of schemas that collectively establish the shape of the data held within a named graph. Others may prefer different definitions. Kurt Cagle goes to show that in the world of knowledge graphs, an ontology is a domain model defining classes and properties. Classes are the types of entities (instances) in the graph, and properties are the attributes and relationships between them. Ontologies define the structure of graphs and allow tools to make more sense of them. Holger Knublauch writes Either way, there seems to be convergence around the notion of using SHACL, the Shapes Constraint Language, for Ontology Modeling. Prior to the introduction of SHACL, knowledge graph validation would mostly be manual or reliant on OWL (Web Ontology Language) constraints. OWL constraints, however, are counter-intuitive. Knublauch followed up on his SHACL tutorial with . and . Radostin Nanov has also written a SHACL guide in 3 parts: , , and . And Ivo Velitchkov with Veronika Heimsbakk maintain a . Part 2 on Qualified Cardinality Constraints Part 3 on SPARQL-based Constraints Learning how SHACL can tame unruly data graphs applying SHACL to data and handling the output the internals of a SHACL engine Wiki on SHACL Bonus track 1: . This report presents a curated collection of ontologies that are highly relevant to ICT domains and vertical sectors, considering their maturity, prominence, and suitability for representing linked data in the semantic web. too. Bonus tracks 2 and 3: and a . A Landscape of Ontologies Standards Landscape Ontologies can contribute in Explainable AI SPARQLing anything SPARQL Wiki Applied Graph AI use cases from DeepMind DeepMind has been one of the forerunners of Graph AI. In the last few months, DeepMind has shared more details about their use of Graph AI in a couple of high-impact use cases. In a paper , DeepMind introduces , a state-of-the-art AI model able to make medium-range weather forecasts with unprecedented accuracy. GraphCast predicts weather conditions up to 10 days in advance more accurately and much faster than the industry gold-standard weather simulation system. published in Science GraphCast GraphCast is a weather forecasting system based on machine learning and Graph Neural Networks (GNNs), which are a particularly useful architecture for processing spatially structured data. DeepMind has enabling scientists and forecasters around the world to benefit billions of people in their everyday lives. open-sourced the model code for GraphCast, is a GNN-based system that discovered 2.2M new crystal structures including about 380k stable structures. Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing. Graph neural networks trained at scale can reach unprecedented levels of generalization, improving the efficiency of materials discovery by an order of magnitude. DeepMind GNoME Bonus track: . A conceptual review of recent key advancements in this emerging field, aiming at optimization and machine learning researchers by DeepMind’s Petar Veličković et.al. Combinatorial optimization and reasoning with Graph Neural Networks Graph Levels of Detail Graph-based data is ubiquitous in enterprises across the industry verticals and is increasingly needed for machine learning use cases. Graph technologies are available, though not quite as widely used yet in comparison with relational databases. Even so, interest in knowledge graph practices has grown recently due to AI applications, given the benefits of leveraging graphs and language models together. A frequent concern is that graph data gets represented at a low level, which tends to make queries more complicated and expensive. There are few mechanisms available — aside from visualizations — for understanding knowledge graphs at different levels of detail. That is to say, how can we work with graph data in more abstract, aggregate perspectives? While we can run queries on graph data to compute aggregate measures, we don’t have programmatic means of “zooming out” to consider a large graph the way that one zooms out when using an online map. This leaves enterprise applications, which by definition must contend with the inherently multiscale nature of large-scale systems, at a distinct disadvantage for leveraging AI applications. a survey of related methods to date for level-of-detail abstractions in graphs, along with indications for future work. Paco Nathan presents Graph and Large Language Models research Wrapping up with a collection of research on various aspects of combining graphs with large language models. Paco Nathan also lists a collection of research efforts to combine Graph ML with Language Models . here IEEE researchers present . Their roadmap consists of three general frameworks. KG-enhanced LLMs, LLM-augmented KGs, and synergized LLMs + KGs. Cf. the previous YotG issue . a roadmap for unifying large language models and knowledge graphs here Similarly, researchers from The Hong Kong University of Science and Technology (Guangzhou), The Chinese University of Hong Kong and Tsinghua University present . They propose a taxonomy that organizes existing methods into three categories based on the role (i.e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks. a survey of graph meets large language model Researchers from ETH Zurich, Cledar, and the Warsaw University of Technology introduce (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of Thought or Tree of Thoughts (ToT). Tony Seale . Graph of Thoughts summarizes the approach Michael Galkin et.al. present . ULTRA can generalize to new KGs of arbitrary entity and relation vocabularies, which serves as a default solution for any KG reasoning problem. Researchers from Monash and Griffith present , a method that synergizes LLMs with KGs to enable faithful and interpretable reasoning. ULTRA, a single pre-trained reasoning model for Knowledge Graph reasoning Reasoning on Graphs (RoG) A growing number of experts from across the industry, including , , and industry analyst firms like , point to Knowledge Graphs as a means for improving LLM response accuracy.  To evaluate this claim, data.world researchers came up with a that examines the positive effects that a Knowledge Graph can have on LLM response accuracy in the enterprise. academia database companies Gartner new benchmark The benchmark compared LLM-generated answers to answers backed by a Knowledge Graph, via data stored in a SQL database. point towards a significant improvement in the accuracy of responses when backed by a Knowledge Graph, in every tested category. Findings is a scalable, inductive, and interpretable path-based Graph Neural Network on Knowledge Graphs. It can be used to make ChatGPT more factual by equipping it with knowledge graph reasoning tools. It’s open source and integrated with ChatGPT. A*Net Google Research presents . A comprehensive study of encoding graph-structured data as text for consumption by LLMs. Talk Like a Graph: Encoding Graphs for Large Language Models Jure Leskovac et al. introduce an end-to-end deep learning approach to directly learn data spread across multiple tables called . The core idea is to view relational tables as a heterogeneous graph, with a node for each row in each table and edges specified by primary-foreign key relations. Relational Deep Learning Subscribe to the Year of the Graph Newsletter Also published . here

The Year of the Graph Newsletter Vol. 25: The Fusion of Generative AI and Graph Technologies

Educational Apps For Kids They say a dictionary is a language in reminiscence, and learning is a window into the past. But, it’s lagging behind both in approach and context. For instance, e-learning apps — they are mammoth available in the market but scarce in classrooms. Also, mobiles are portrayed as cheat-devices, distractors, and addictive gadgets and restricted from the classrooms. And, that is distressing, given the potential education apps hold for learners and schoolings — inspiration and time for mentors, motivation and support for learners, transparency for parents and high ROI for farsighted businesses. Once Benjamin Franklin said, “An investment in knowledge pays the best return”. So, if the above quote holds true and blue, education lane is like burning the candle at both ends to the ROI (Return On Investment). Are you looking for proof? Let’s take at present the which is much older than you think. And, it’s not far that it will be cut into two. The precedents are already there. With easy access to the right tools and knowledge, kids step out from their classroom as a millionaire — merely at 15! average age of successful entrepreneurs So, rather than discussing disruption, it’s high time to align with the best and unfurl intricacies of creating a groundbreaking educational app. app development company After all, thinking about creating an awesome learning app is a promising move for future. And, herein we are going to help you hunt down answers to questions of your interests: What lion share does educational app capture? What is the prerequisite of design and product excellence? What will be the development options? How to monetize educational apps? What Lion Share Do Educational Apps Capture? According to Statista, the e-learning market globally is predicted to eclipse 37.8 billion U.S dollars in 2020. The educational app numbers approaches approximately to 936 million downloads presently — 470 million on Apple app store and 466 million on Google play store by the Q1 of 2020. Image Source To offer a tip of it’s diversity, here’s a brief description of product categorisation by Edsurge. Educational software usually funnels down to five categories. School Operations: This category is basically designed for use by higher designated officials like principals and school administrators to manage schools and educational process participants. Curriculum Products: This category involves help with teaching concepts and skills. Curriculum products ask for compliance, especially with the transformation in Common Core Standards. Post-Secondary: The post-secondary category aims at learners, admins, professors, and also parents involved in post-secondary learning surrounding. Teacher Requirements: This array of educational software helps the teacher with classroom-related activities like lesson and lecture planning, classroom management, grading system and a lot of other stuff. The Left Out: This category sheath the complimentary K-12 curriculum products or anything else related with the education aisle. To your surprise, you will be amazed to see the distinctiveness in architecture and content of numerous e-learning mobile app: Educational games Subject-oriented learning apps Multi-purpose learning apps Learning apps for preschoolers and toddlers Management learning apps Taking into consideration the objective, the app varies from instructive (drill-and-practice) with the help of experimentation and discovery to constructive ones, in regards to using Bloom’s taxonomy of learning to write practical learning objectives. Many among them aim at knowledge and skillset, while others focus on collaboration and learning and teaching support, and the list is never-ending. Keep in mind; the consumer market covers several needs — from kids to teenagers, specially-abled kids app, corporate training tools and what else you can think of. But the number and diversity of learning apps are not surely assuring you quality. Indeed, the first generation of mobile learning app has shown there is a behemoth area of advancement. So,without any further ado, let these insights guide you through the education app development aisle. Step 1: Craft For Quality and Excellence Firstly, integrating an engineering-style and problem-solution thinking to complicated domains like education is a big challenge. But, knowing how to align the app’s design and use the processes of learning and developing requires a proper understanding of the learning science with the right assistance from an experienced to power decisions that matter. app development company Here’s Seven Vertical Of Creating An Educational App To Keep In Mind In an e-learning app architecture, these seven dimension transform the product roadmap: Single/Bi-directional information flow Communication: Open to public / confine to a specific group Public: Information adjusted based on user identity Identity: Asynchronous/Synchronous user interaction Temporal: Potential of in-app purchases Transaction: The piece of information or feature set customized for user location Location: The app solely / a group of other participants Multiplicity: The Crucial Shafts Of E-learning App Design Do you think content is enough? No, it’s not! Designing the right learning flow is equally important. Have you ever wondered what makes an educational app truly “learning” by design and nature? Humans quickly learn best when they: Socially Interact with others in high-quality approaches Actively involve with curious heads Engage with the intuitive learning materials Have meaningful experiences In the end, the that create an excellent educational app to promote active, highly engaging, meaningful, and socially interactive learning are the winners. And you can too! mobile app developers Step 2: Know Your Development Choices Understanding a ballpark for the time and cost to create an educational app makes it a lot easier to scrutinize the budget before heading starting with the development process. But simultaneously two similar projects can gradually vary in cost. You just simply can’t assign features a set price and expect the price to be the same across every project. The advises developing just one screen of a mobile app taking about a week with a full-time employee (FTE). This consists of planning, development, and then testing and final approval, so a mobile app is developed in two months. research The trusted offer a ballpark of cost by multiplying the hourly salary of FTE with the time taken. And, that’s excluding other overhead expenses like developers license for app creation tools. app development company So, when you pen down the rough sketch for creating an educational app, the below listed parameters are imperatives. Have a look: Registration/ Log-in Instructional Overlay User Profile/Dashboard Search Filters Push Notifications Social Media Integrations Database Offline Mode Motivational Feature Set Communication and Interaction Module Audio/Video Streaming Education App For Kids 2021’s Generation Technologies For Educational App The latest editions in technology stack and approaches have made their way to educational apps. AR Captivates All Senses Whether you agree or not, Augmented Reality amplifies real-world experiences. Be it an amusement park or an art gallery; AR technologies hold the potential to uphold any situation, location, surrounding or even venture to a totally new level. AR is gradually transforming the way people interact and learn with mobile devices. Besides, the biggest reason love it is because ideas for AR are almost everywhere — just have a look around. Explore night from a new aspect with a , or go to the woods with a , or feel free to learn new languages on the go with . mobile app developers Star Chart Leafsnap Google Translate There are also tools for mentors and students available on to establish their own AR & VR content. Students can visualize any topic on subjects varying from history to astronomical science. Teachers can create curriculum and monitor, assess and correct students at the weak points with the help of the app. EON Creator AVR Artificial Intelligence Is The Key To Accelerate Learning Capability Image Source You must be wondering, “is your teaching about to be disrupted”? Yes, it is. But in a better way. With the debut of AI, every aspect of education is revolutionizing, from curriculum development all to assessment, smart algorithms are adjusted according to the learner’s abilities. . No more averages, everyone gets the best. As the value of AI technology starts to penetrate the customer/user/learner experience, replying to customer demands will become a decisive force, with an average of one in three citing customer demands as a significant reason for AI adoption, approximately for 50% of the dwelling from education organizations. Lastly, Education apps are the forerunner in AI adoption with services and enablers having 5–10% lower adoption rates. Image Source So, undoubtedly, AI creates mammoth value for the organization as a tool for assessment and reviews, and as a tool for educational apps. Moreover, the passionate offer an ideal blend of intelligence, for instance, a distributed learning strategy aiming to integrate live classroom methods with the right virtual technologies consisting of virtual facilitators, intelligent tutor, augmented reality and other exclusive features. mobile app developers The Gaming Element For Non-Gaming Context Motivation is quite crucial in learning and education. Still, it often comes with challenges in conventional classrooms, like challenges in games were getting some arbitrary value or reaching the next level have little to no practical value. Fortunately, the primary elements of gaming and learning to eclipse sometime — especially for setting objectives, developing a strategy, and getting rewards are the common things. And, it’s not just for kids app, gamification has been significant in employee performance, customer engagement, and also crowdsourcing initiatives. For better understanding, let’s take an example of a at Webster University. The app notifies freshmen on a scavenger hunt with the help of a smartphone. And, with customizable treks and challenges, it’s informative and interactive, in short, a great alternative to monotonous handouts. location-based mobile app for orientation Streaming Going Viral In The Corporate Learning Presently, corporate learning is inclined towards . Also, Josh Bersin said, “the connection between corporate learning and video/audio streaming services is not anymore a far-sighted future”. education ‘in the flow of work’ Modern learning methods transform a series of playlist and channels that you subscribe to and get customized AI-powered recommendations, similar to Netflix. Businesses grab the value of micro-learning and in return, provide their own video-learning platforms. Step 3: Monetize Your Educational App When a reliable mobile app developer creates your excellent educational app, monetization takes time. For beginners, the Free model is perfect for alluring clients. Simply, because of the plentiful availability of the apps of the same category, that’s the only option available. The minimum you can do is offer a free trial to get to know your app. But, creating reliable revenue streams is an unskippable aspect of app success, so weigh your choices. The paid features are where mostly free educational apps scale new heights. You cannot get VC injections forever. So, better to think of some compelling features outside your MVP (Minimum Viable Product) that users will happily pay for. Freebie: If you offer a platform for user-generated content, you can always change an interest for a course viewing or a commission for posting a paid course. Commissions: A monthly subscription is a popular choice for monetizing an e-learning app. Alternatively, introduce numerous user plans for access to different functionalities (free/VIP/premium/pro). Subscriptions: In-App ads are bothersome so it’s advisable to use them in moderation. Ensure you know the ad policy for your potential target users, especially if you are creating it for toddlers and kids. Advertisement Revenue: Advertising towards children is rightly limited by strict rules and regulations like and the advertising industries own self formulated regulation through the . Children’s Online Privacy Protection Act (COPPA) Children’s Advertising Review Unit (CARU) Providing paid consumables within an app sometimes can be a red flag, especially with kids apps, as many parents find it daunting to safeguard their wallets. In-App Purchases: Also, app stores offer parents guidance and needed tools against the strategy. So, gracefully offering users a choice to opt-out will provide you with more credit than being confined and manipulative. The other way round is charge once for the app’s download. Paid model is only viable if you aim at the mammoth size market. Keep in mind to offer a free-trial as no one enjoy a pig in the poke. Paid Downloads: Final Thought Although the pandemic has taken its levy on everybody, there is no more need to be a hostage to the situation. But, with , it’s worth continuing to funnel down new opportunities and optimally use the situation to craft new projects and ideas. mobile app developers Also published at https://loricaricofe.medium.com/2021s-cost-feature-analysis-to-create-an-educational-app-8327614873d4

Cost and Feature Analysis for Creating Educational Apps in 2021

Data science podcasts seem to have exploded in popularity overnight. They manage to fit perfectly into our busy lifestyles. They are both informative and entertaining. Plus, you can listen to them anytime, anywhere. We can say that podcasts are undeniably a fantastic source of updates for every aspiring professional in the field. But where can you find the topics most relevant to your interests? Don’t worry. 365 Data Science is here to help you with that. In this article, we will review the top 15 Data Science Podcasts worth listening to, according to our research. Those are sure to keep you in the loop about the latest data science developments and trends. Why? Because data science is a constantly expanding field. For this reason, there is a growing number of experts who use blogs and podcasts to share their thoughts on topics ranging from to data optimization. So, we’ll review the resources and make it easier for you to take your pick. Machine Learning We will give: Description of the featured topics;Frequency of updates;Average length of episodes;Brief information about the hosts. So let’s jump right into to it! Note: Our top picks are not arranged by ranking. So, rather than looking for the most popular podcast out there, use the list below to find the ones that match your interests. Which Data Science Podcasts will keep you up-to-date? Linear Digressions Linear Digressions is a good-humored podcast devoted to Machine Learning and Data Science. After their signature greeting: “Hey, Katie”, “Hey, Jeff”, the hosts delve into friendly discussions of big topics. They both often cover AI, Machine Learning, Deep Learning, convex and non-convex optimization, and data engineering. Although the podcast doesn’t feature any guests, it includes useful relevant links that provide extra information for each topic. Featured topics: weekly Update frequency: 15-30 min Episode length: Ben Jaffe – an UI Engineer who spent many years as a front-end web developer and Caitlin (Katie) Malone, Ph.D. in Experimental Particle Physics at Stanford University and Director of Data Science Research and Development at Civis Analytics. Hosts: Plumbers of Data Science So, if you are into data engineering and want to keep track of the trends around big data, this podcast is for you. Outspoken Kretz doesn’t shy away from giving his honest opinion. Furthermore, he examines various tools and techniques used in Data Engineering. We can say that this is super helpful for data scientists who are just getting started. By the way, you can also learn more about traditional and big data in our article . Featured topics: Techniques for Processing Traditional and Big data pretty random Update frequency: up to 30 min Episode length: Andreas Kretz – a Data Engineer and Big Data professional. His data science platforms process and analyze immense amounts of data on a daily basis. Host: Data Skeptic The Data Skeptic podcast features long-form interviews with the best experts in the field. Their discussions provide insights into all things data science. In fact, the host is truly devoted to applying critical thinking and exploring the plausibility of claims. Furthermore, the show has hands down the cutest mascot in data science – a parrot called Yoshi who also happens to be the focus of numerous mini-discussions. Featured topics: Regardless of your background, you will find plenty of accessible knowledge. You’ll also learn a whole lot about data mining, algorithms and search engines. Furthermore, you’ll discover plenty about retrieavals, forcasting, regression, image recognition, natural language processing, game theory, and so much more. Each podcast episode includes show notes, data files, plots or short amounts of code you can access on a public GitHub page. weekly Update frequency: We can say that most interviews last around 30 minutes. However, if you are new to data science, opt for the 15-minute mini-episodes first. Episode length: Kyle Polich – a computer scientist with a truly wide scope of interests.  They range from AI, machine learning, and statistics to data provenance, data governance, econometrics, and metrology. Linh Da Tran co-hosts the mini-episodes. The latter will be really beneficial for you, in case you don’t have any data science background. Furthermore, if you are passionate about statistics, make sure you check out our . It’s totally worth it. Hosts: Statistics Tutorials Talking Machines – Human conversations about machine learning Talking Machines lifts the curtain on machine learning, computer science, and deep learning. Every two weeks, the hosts lead you through diverse insightful conversations with experts in the field. You can also find discussions about plenty of industry news. Moreover, the podcast provides helpful answers to listeners’ questions about internships vs salaried positions, the update to the ACM code of ethics, the next AI winter and if/when it is coming, and more. Featured Topics: twice per month Update frequency: 30 min – 1 hour Episode length: Katherine Gorman – a former public radio producer and Neil Lawrence. The latter also leads Amazon Research Cambridge, where he is a Director of Machine Learning. Hosts: O’Reilly Data Show This is an excellent podcast for those of you who want to dig deeper into data science. The episodes will help you explore the opportunities and techniques driving big data, data science, and AI. Interviews with distinguished CEOs and co-founders will keep you up-to-speed with the trends in data, machine learning, AI, deep neural networks, large-scale computation, natural language processing, and the like. Featured topics : 2-3 per month Update frequency: 30-50 min Episode length: Ben Lorica – the Chief Data Scientist at O’Reilly Media, Inc. He is also a Program Director of both the Strata Data Conference and the Artificial Intelligence Conference. Host: Not So Standard Deviations Not So Standard Deviations is my personal favorite. Co-host Hilary Parker’s entertaining comments about her barely being a data scientist clearly show that data people are far from taking themselves seriously all the time. The podcast brings you the latest in data science and data analysis in academia and industry. Podcast topics include the distinction between what data analysis is and what data analysts do, the connection between business intelligence analysts and data scientists, and quality in data analysis . You can also find reviews of data science books and information about upcoming events. Co-host Hilary shares her secret trick for data analysis, what computer to buy, and how to get into data science. Featured topics: twice per month Update frequency: 1 hour Episode length: Roger Peng – a Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He is also the author of the book R Programming for Data Science. Peng’s charming co-host is Data Scientist Hilary Parker of Stitch Fix. Hosts: Data Stories So, If you’re looking for engaging storytelling on top of your data visualization updates, you will definitely enjoy this podcast. Data Stories is a cool source for data visualization insights. Guests on the show discuss a wide variety of intriguing subjects, such as the general role of data in our lives, xenographics, and what makes a visualization memorable. Furthermore, you will find out more about machine learning for artists, touch graphics, data sculptures, indexical visualization, and data visualization literacy. Featured topics: twice per month Update Frequency: 40-45 min Episode length: Enrico Bertini- an acclaimed researcher in the field of data visualization with a background in Computer Science. Bertini lives in New York and, in his own words, is working hard on keeping his Italian accent. His co-host is Moritz Stefaner – an independent data visualization designer and a fan of horses, bees and trips around the globe. Hosts: Data Science at Home Here’s a podcast that aims to make machine learning and AI effortless for everyone. You can learn a lot about reproducible machine learning pipelines, the predictive power of metadata, deep learning and image compression, predicting the weather with deep learning, and even the host’s take on the reasons to aggregate data for precision medicine and the consequences of such strategies. Plus, if you are a fan of smooth jazz, you’re going to appreciate the background music in the first episodes. Featured topics: 1-3 episodes per month Update frequency: 20 min Episode length: Francesco Gadaleta, Ph.D. He’s an AI architect and Computer Science researcher. Host: Learning Machines 101 – A Gentle Introduction to Artificial Intelligence and Machine Learning This Data Science Podcast examines questions like , and . Richard Golden also teaches you how to build a machine that learns to play checkers and how to transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine. Featured Topics: how do AI devices work how can we make them even smarter and more human-like monthly Update Frequency: 20-30 min Episode length: Richard Golden, Ph.D. Host: Data Futurology Here, experienced data science leaders from around the globe share their stories, challenges, and valuable experience gathered throughout their career. You will learn about the skills required to be a great data scientist. Furthermore, you will get valuable tips about how to build a world-class data science team. Other topics include implementation of data strategies and applications of ML/AI in your industry. In addition, to discover more about what it takes to become a data scientist, take a look at Featured topics: The Data Scientist Profile 2019 weekly Update frequency: usually 1 hour Episode length: Felipe Flores – GM Data Science & Analytics, Speaker and Advisor Host: Data Able Dave and Matt feature guests who share insights about the best practices for getting value out of data for organizations. The podcast also includes discussions about the improvement of higher education through data, the importance of data storytelling, and much more. Featured Topics: weekly Update frequency: 9 to 45 min Episode length: Dave Mathias – an experienced data advisor and data analyst, and Matt Jesser, who holds a master’s degree in Industrial-Organizational Psychology Host: Bright Lights Big Data Bright Lights Big Data gives you the chance to learn pretty fascinating stuff about statistical uncertainty and weather forecasts correlation. Moreover, you will receive useful book recommendations and discover the latest on big data, smart cities, machine learning, and equity. Featured topics: twice per month Update frequency: 30 min Episode length: Urban planner Mike Armstrong and data scientist Tammy Armstrong. Host: The Data Lab Podcast Though updates are far from regular, you should definitely take a listen at this podcast. Furthermore, you can count on staying up-to-date with award-winning data science projects and data science recruitment tips shared by the podcast’s data scientist guests. Featured topics: random Update frequency: 20 min to about an hour, depending on the guest Episode length: The Data Lab team, the national innovation center for data and AI in Scotland. Host: TAG Data Talk If you need fresh outlook on the realm of data science and analytics, you might want to stop by and listen to this podcast. It covers multiple topics on the current outlook of analytics and data science. Moreover, professionals and academics in the field discuss talents and skills, organizational structures, and tool advancements related to data science and analytics. You can also stumble on advice on how to effectively communicate analytics results, frame business problems for analytics, and organize and develop successful data science functions. Featured Topics: monthly Update frequency: 20 min Episode length: Beverly Wright, Ph.D. and Chief Analytics Officer Host: More or less behind the stats – Tim Hartford “the Undercover Economist” Those of you who live and breathe statistical data will find what they are looking for in Tim Hartford’s podcast. It presents a broad range of subjects, including economics, politics, business, and health care. Plus, you’ll learn the all the hidden statistical truths behind them. Featured topics: weekly Update frequency: 10-20 min Episode length: Tim Hartford – a renowned economist, journalist, and broadcaster. He’s also the author of “Fifty Things That Made the Modern Economy”, “Messy”, and the million-selling “The Undercover Economist”. Host: Bonus Podcast: Women in Data Science (WiDS) Podcast Now that’s an up-and-coming data science podcast worth giving ear to. As part of the worldwide , it aims to educate and inspire women in data science. What’s more, it offers support for data scientists, regardless of their gender. WiDS started as a technical conference at Stanford in 2015. Now, it includes more than 150 regional events across the globe, and a datathon. The features female leaders in the field sharing their career highlights, advice, and lessons learned along the way Featured topics: Women in Data Science initiative WiDS podcast . seasonal Update frequency: 30 min Episode length: Margot Gerritsen is a Stanford Professor of Energy Resources Engineering, Co-Founder and Co-Director of the Women in Data (WiDS) Conference, and Host of the WiDS Podcast. Host: That sums up our list of Top 15 Data Science Podcast. We hope our selection will prove useful to you on your way to becoming an expert data scientist. However, just keeping up-to-date with the trends is not enough to get you started on your successful career path as a data scientist. Qualifications, background, and experience are of paramount importance. So, take a page out of the data scientist’s book and . take a look at what the data tells us you need to become a data scientist in 2019 Then, if you’re still hungry for weekly data science updates, browse our for more inspiration. top 51 data science blogs list

15 Data Science Podcasts Worth Checking Out

Data science and programming are such rapidly expanding specialities it is hard to keep up with all the articles that come out from Google, Uber, Netflix and one off engineers. We have been reading several over the past few weeks and wanted to share some of our top blog posts for this week April 2019! We hope you enjoy these articles. Building and Scaling Data Lineage at Netflix By: , , Di Lin Girish Lingappa Jitender Aswani yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question — “Can I run a check myself to understand what data is behind this metric?”Now, imagine yourself in the role of a software engineer responsible for a micro-service which publishes data consumed by few critical customer facing services (e.g. billing). You are about to make structural changes to the data and want to know who and what downstream to your service will be impacted. Imagine Read More Here DeepMind and Google: the battle to control artificial intelligence By Hal Hodson One afternoon in August 2010, in a conference hall perched on the edge of San Francisco Bay, a 34-year-old Londoner called Demis Hassabis took to the stage. Walking to the podium with the deliberate gait of a man trying to control his nerves, he pursed his lips into a brief smile and began to speak: “So today I’m going to be talking about different approaches to building…” He stalled, as though just realizing that he was stating his momentous ambition out loud. And then he said it: “AGI”. Read More Here Learning Data Science: Our Favorite Resources From Free To Not Today we wanted to cover some of our favorite resources for data science. As the title suggests, these resources will be from free to not. Some people like buying books and other people prefer online courses. So we have created this list of data resources that range from books to courses, from free to not. Data science has many facets. Statistics, data cleansing, programming, system design and really…almost anything else data related depending on how large the company is. This post will discuss our favorite resources for these topics. Now, most of these courses and books are primers for topics like statistics, Python and data science in general. They really will only provide the base knowledge. At the end of the day, real practical experience is one for the few things that will really train your data science knowledge. You should learn as much as you can from these resources and then apply for as many internships and entry-level positions as possible and study for interviews. re Read More He Object Detection with 10 lines of code By Moses Olafenwa One of the important fields of Artificial Intelligence is Computer Vision. Computer Vision is the science of computers and software systems that can recognize and understand images and scenes. Computer Vision is also composed of various aspects such as image recognition, object detection, image generation, image super-resolution and more. Object detection is probably the most profound aspect of computer vision due the number practical use cases. In this tutorial, I will briefly introduce the concept of modern object detection, challenges faced by software developers, the solution my team has provided as well as code tutorials to perform high performance object detection. Read More Here How Apache Airflow Distributes Jobs on Celery workers By Hugo Lime Discover what happens when performs task distribution on workers through queues. Apache Airflow Celery RabbitMQ is a tool to create workflows such as an . A workflow is a directed acyclic graph (DAG) of tasks and Airflow has the ability to distribute tasks on a cluster of nodes. Let’s see how it does that. Apache Airflow extract-load-transform pipeline on AWS Read More Here Capturing Special Video Moments with Google Photos Recording video of memorable moments to share with friends and loved ones has become commonplace. But as anyone with a sizable video library can tell you, it’s a time consuming task to go through all that raw footage searching for the perfect clips to relive or share with family and friends. Google Photos makes this easier by automatically finding magical moments in your videos — like when your child blows out the candle or when your friend jumps into a pool — and creating animations from them that you can easily share with friends and family. Read More Here Uber Case Study: Choosing the Right HDFS File Format for Your Apache Spark Jobs By Scott Short As part of our effort to create better user experiences on our platform, members of our Maps Data Collection team use a dedicated mobile application to collect imagery and its associated metadata to . For example, our team captures images of street signs to in order to facilitate a more seamless trip experience… enhance our maps improve the efficiency and quality of our maps data Read More Here You created a machine learning application. Now make sure it’s secure. By and Ben Lorica Mike Loukides In a , we described what it would take to build a sustainable machine learning practice. By “sustainable,” we mean projects that aren’t just proofs of concepts or experiments. A sustainable practice means projects that are integral to an organization’s mission: projects by which an organization lives or dies. These projects are built and supported by a stable team of engineers, and supported by a management team that understands what machine learning is, why it’s important, and what it’s capable of accomplishing. recent post Read More Here Developing A Data Science Career Framework By Adam McElhinney At Uptake, Data Scientists are at the core of what we do. To that end, it’s very important that we have a good definition of the following: what does a Data Scientist do; how is a Data Scientist’s performance evaluated; and how does a Data Scientist progress in their career. Once you have these definitions, they can be used as the basis for all of your hiring, development, compensation, exit and promotion decisions. Read More Here Diagnosing Heart Disease Using ML Explainability Tools and Techniques By Rob Harrand IntroductionOf all the applications of machine-learning, diagnosing any serious disease using a black box is always going to be a hard sell. If the output from a model is the particular course of treatment (potentially with side-effects), or surgery, or the absence of treatment, people are going to want to know . why This dataset gives a number of variables along with a target condition of having or not having heart disease. Below, the data is first used in a simple random forest model, and then the model is investigated using ML explainability tools and techniques. Read More Here Thank you so much for reading. If you are interested in getting updates about our favorite articles then sign up here for weekly newsletters.

10 Great Articles On Data Science And Data Engineering

Graph databases are the hottest thing around right now. Whether you are just getting started, or you are in one of the 51% of organizations already using them, this is the place to get your news and analysis. The popularity of graph databases has gone through the roof almost overnight it seems. Everything points this way: the , the , the response to my . trend lines from database engines reports from the Forresters of the world graph-related ZDNet posts Why is that, and should you care? Do you really need a graph database, and if yes, how do you choose one? That’s the million dollar question. Well, that’s more than one questions actually, and that’s more than a million in value there too. Either way, i can help you answer, starting today. This is the first edition of the monthly Year of the Graph newsletter. Every month i will collect, republish, and comment on the 10 most important Graph database related news items. Why me? Ever since i implemented my first graph database prototype in 2005, i have worked on award-winning research, consulted the (then) leading vendor on distributed query implementation, and lead teams of all sizes and shapes working with graph databases. I have also published a number of reports and articles with analysis on big data, distributed systems and analytics with and . Perhaps more importantly though: Gigaom ZDNet I work for nobody else but me, and by extension, you. No fluff, just stuff. No automatically harvested or sponsored posts. No vendor affiliations or hidden agendas. Just hand picked, curated content, and objective, concise analysis. Why the year of the graph? Glad you asked. In a nutshell, because graph databases are coming of age and getting attention. Some of it has to do with the infrastructure and the technology enabling graph to get mainstream. The use cases have always been there, and heavyweights like AWS and Microsoft are moving in this space. _It's official: graph databases are a thing. That's the consensus here on Big on Data among fellow contributors Andrew…_www.zdnet.com The year of the graph: Getting graphic, going native, reshaping the landscape | ZDNet 2. How do you model you graph? This is the question Dilyan Damyanov from Snowplow explores. Using graphs is part of what Snowplow does, with an emphasis on managing events. The analysis on different answers on how to model event data as a graph sheds some light on the fine art of modelling graphs. _In recent months we've been busy expanding the variety of storage targets available for Snowplow users to load Snowplow…_snowplowanalytics.com Building a model for event data as a graph - Snowplow 3. So, you have a Hadoop data lake. There’s graph data in there, and “graphy” queries you can do on that data. ArangoDB’s Max Neunhöffer gives examples of graph use cases, and provides an introduction to some graph algorithms you can use, as well as a walkthrough of how to get started with ArangoDB. _Graphs in the sense of linked data with vertices and edges are everywhere these days: social networks, computer…_www.oreilly.com Fishing for graphs in a Hadoop data lake 4. Graph is a much more natural and efficient paradigm for doing multiple joins (hops) than relational data. Furthermore, graphs also work well together with another mega-trend, machine learning. The inherent structure in graphs can be leveraged in your machine learning algorithms, as Graphistry’s Leo Meyerovich discusses with O’Reilly’s Ben Lorica. _The O'Reilly Data Show Podcast: Leo Meyerovich on building large-scale, interactive applications that enable visual…_www.oreilly.com Graphs as the front end for machine learning 5. In their original inception data lakes grant universal access to data in their native formats, yet lack the necessary metadata and semantic consistency for long term sustainability. What can give you that? The combination of enterprise-wide ontologies, taxonomies, and terminology, says Franz Inc’s Jan Aasman. _One of the most significant results of the big data era is the broadening diversity of data types required to solidify…_www.infoworld.com Harmonizing big data with an enterprise knowledge graph 6. Graph databases come in 2 main flavors. RDF is one of them, and Linked Data is a collection of related standards, including taxonomies, that enable graph-based navigation and querying. If you’re interested in the foundamendals of this approach, this account of the Network for Information and Knowledge Exchange’s workshop may be for you. _Conrad Taylor writes: The first NetIKX meeting of 2018, on 25 January, looked at new technologies and approaches to…_netikx.wordpress.com Making true connections in a complex world - Graph database technology and Linked Open Data 7. If you think a taxonomy sounds fancy, how about an ontology? As Teodora Petkova from Ontotext argues, this is not about a philosophical debate on the essence of being. In order to define what something “is” (to a computer program), information technology resorts to the use of ontologies. Some graph databases use those — this is how you build a knowledge graph. _A program that wants to compare or combine information across the two databases has to know that these two terms are…_ontotext.com Ontology, Meaning and Knowledge Management 8. The other graph database flavor is LPG — Labelled Property Graphs. One of the differences between RDF and LPG is support for taxonomies and ontologies, and as a consequence of this, inference: RDF has it, LPG does not. Or at least, that was the case up to now, says Thorsten Liebig from derivo GmbH. Their solution wants to support RDF-like reasoning on LPG. _Discover how the Neo4j graph database and the Cypher graph query language perform as an OWL reasoning engine when…_neo4j.com Neo4j: A Reasonable RDF Graph Database & Reasoning Engine [Community Post] 9. Titan, an open source LPG graph database, was an important piece of the graph database world. As Titan is no longer maintained, IBM and others have been supporting a group of people who stepped up and adopted its codebase, forking it as JanusGraph. Ted Wilmes from Expero is one of the architects of JanusGraph, and he recently spoke about the state of JanusGraph in 2018. 10. What is the current status quo in the graph database world? All the latest developments from AWS, Cambridge Semantics, Neo4j and Tigergraph, in the “ best overview of the graph database world(s) so far “, plus analysis on the options for querying graphs. _Fifty years ago, relational databases were neither ubiquitous nor standardized. The story of how we went from the…_www.zdnet.com Back to the future: Does graph database success hang on query language? | ZDNet Would you like to receive the latest Year of the Graph Newsletter in your inbox? Easy — just signup below. Have some news you think should be featured in an upcoming newsletter? Easy too — drop me a line here . Originally published at http://linkeddataorchestration.com/2018/04/03/yearofthegraph/

The Year of the Graph Newsletter: April 2018

Artificial Intelligence/Machine field is getting a lot of attention right now, and knowing where to start can be a little difficult. Learning I’ve been dabbling in this field, so I thought of curating the best resources in one place. All of these are curated based on if it’s an inspiring read or a valuable resource. I hope this curated list help you get started on what you need to know about /Machine Learning on a technical level. AI Inspiring AI/ML Reads _Years ago I had coffee with a friend who ran a startup. He had just turned 40. His father was ill, his back was sore…_www.technologyreview.com Are We Smart Enough to Control Artificial Intelligence? _Artificial intelligence is all the rage, with headline-grabbing advances being announced at a dizzying pace, and…_www.technologyreview.com AI winter isn't coming, says Baidu's Andrew Ng _Four days later, a couple of hundred journalists, entrepreneurs and advertisers from all over the world gathered in…_www.nytimes.com The Great A.I. Awakening _PDF: We made a fancy PDF of this post for printing and offline viewing. Buy it here. (Or see a preview.) Note: The…_meetnucleus.com The Artificial Intelligence Revolution: Part 1 - Wait But Why _PDF: We made a fancy PDF of this post for printing and offline viewing. Buy it here. (Or see a preview.) ___________ We…_meetnucleus.com The Artificial Intelligence Revolution: Part 2 - Wait But Why _One of the most interesting topics in modern times is the "robots eat all the jobs" thesis. It boils down to this…_blog.pmarca.com This is Probably a Good Time to Say That I Don't Believe Robots Will Eat All the Jobs ... _Six areas of AI that are particularly noteworthy in their ability to impact the future of digital products and services…_medium.com 6 areas of AI and machine learning to watch closely _How I learned deep learning in 10 weeks and won $5,000 The images above are examples of the three possible classes I…_meetnucleus.com Recognizing Traffic Lights With Deep Learning _Many have written about how deep learning is taking over the world and why that is important; I cannot echo them enough…_meetnucleus.com Deep Learning is Revolutionary - Transmission Newsletter _One of the biggest confusions about “Artificial Intelligence” is that it is a very vague term. That’s because…_medium.com The Many Tribes of Artificial Intelligence _Machine Learning - This is the second article in a larger series exploring the intersection of design and existing…_meetnucleus.com Understanding How Machines Learn, Through Prototyping - Big Tomorrow _Would be naive to think that it’s just our personal lives that are dramatically enhanced by the recent progress in…_medium.com Artificial Intelligence At Work: The Future Of Workplace _AI and related technologies are the next major technology enablers, just like mobile, social and cloud before them._medium.com Why go long on artificial intelligence? _Part 1 of a three-part series on the “Future of Ecommerce Search”_blog.semantics3.com Here’s what Consumers Expect in the Age of AI _From tools, to research, to ethics, Ben Lorica looks at what's in store for artificial intelligence in 2017. Get the O…_meetnucleus.com 7 AI trends to watch in 2017 _In this industry, it's a tired old cliche to say that we're building the future. But that's true now more than at any…_www.oreilly.com The ethics of artificial intelligence _Teaching Your App To Detect Traffic Lights From 18,000 Images Is The First Step In Building Your Own Self Driving Car…_meetnucleus.com Bring Magic To Your Mobile App With Deep Learning _Yes, millions of low-paying, low-skilled jobs are increasingly at risk. But there’s also much to gain from the coming…_backchannel.com The AI Takeover Is Coming. Let’s Embrace It. _If you're not using deep learning already, you should be. That was the message from legendary Google engineer Jeff Dean…_hbr.org Machine Learning Is No Longer Just for Experts _If you’re like me, you’re fascinated with AI._hackernoon.com Learning AI if You Suck at Math _It's New Year's 2017, so time to make predictions. Portfolio diversification has never been me, so I'll make just one…_meetnucleus.com GANs will change the world _Excerpted from Last Week in the Future V3.0: The most interesting news in IoT, AI, and other tech fields from last week…_iot-for-all.com Democratizing Machine Learning, Looking at Bots, and Rethinking the Web _(An alternate version of this article was originally published in the Boston Globe)_medium.com Deep Learning Is Going to Teach Us All the Lesson of Our Lives: Jobs Are for Machines _On the west coast of Australia, Amanda Hodgson is launching drones out towards the Indian Ocean so that they can…_www.wired.com 2016: The Year That Deep Learning Took Over the Internet AI/ML Tools & Resources Best Practices for ML Engineering Learn TensorFlow and deep learning, without a Ph.D. Fueling the Gold Rush: The Greatest Public Datasets for AI Attacking machine learning with adversarial examples Gilt ♥ Open Source / Deep Learning at GILT A Beginner’s Guide To Understanding Convolutional Neural Networks Unsupervised Feature Learning and Deep Learning Tutorial Machine Learning is Fun: Modern Face Recognition with Deep Learning Image Completion with Deep Learning in TensorFlow How to Code and Understand DeepMind’s Neural Stack Machine Hacker Noon: Artificial Intelligence AI/ML Learning Courses Learn the fundamentals of Artificial Intelligence (AI). Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems. Artificial Intelligence (AI) — Master the essentials of machine learning and algorithms to help improve learning from data without human intervention. Machine Learning . — More than 4M views on Youtube and iTunes. Featured on edX. Free Course, Lecture Videos Available. Learning From Data — Online Course (MOOC) Free Course with Lecture Slides and Video recordingsDepartment of Computer Science, 2014–2015, ml, Machine Learning. Machine Learning — Master machine learning fundamentals in four hands-on courses. Machine Learning — University of Washington — Learn about artificial neural networks and how they’re being applied to speech and object recognition, image segmentation, modeling language and human motion. Free Course. Neural Networks for Machine Learning — Free course created by Stanford University, taught by Andrew Ng. Machine Learning — Stanford University — AI/ML Videos A philosopher and technologist, Bostrom asks us to think hard about the world we’re building right now, driven by thinking machines. Will our smart machines help to preserve humanity and our values — or will they have values of their own? What happens when we teach a computer how to learn? Technologist Jeremy Howard shares some surprising new developments in the fast-moving field of deep learning. Years of work down the drain, the convolutional neural network is a step change in image classification accuracy. Image Analyst Dr Mike Pound explains what it does. Mike Tyka, who is both artist and computer scientist, talks about the power of neural networks. These algorithms are capable to transform computers into artists that can generate breathtaking paintings, music and even poetry. Hugo Larochelle shares his observations of what’s been made possible with the underpinnings of Deep Learning. This is lecture 1 of course 6.S094: Deep Learning for Self-Driving Cars taught in Winter 2017. During his talk, Professor Ng discussed how artificial intelligence (AI) is transforming industry after industry. AI/ML Blogs & Newsletters Machine learning and deep learning blog (7.1k subscribers). Machine Learning Weekly — A free weekly newsletter featuring curated news, articles and jobs related to Data Science. Data Science Weekly Newsletter — A Weekly Roundup of ML & AI News. Machine Learning and AI are having a huge impact on our lives. Machine Learnings — AI & Deep Learning Newsletter. Artificial Intelligence News — Machine learning and artificial intelligence blog. When trees fall… — AI, Deep Learning, NLP Bl_og_ WildML — Neural network focussed blog. C olah’s blog — A machine learning craftsmanship blog by @iamtrask i am trask — Did I miss any awesome blogs/videos/resources? Let me know on or comments. Twitter Found this post useful? Kindly tap the ❤ button below! :) This list was originally curated on . You can check out more awesome machine learning resources . Nucleus here

Index of Best AI/Machine Learning Resources

London, United Kingdom, May 13th, 2024, CyberNewsWire Logicalis, the global technology service provider delivering next-generation digital managed services, has today announced the launch of Intelligent Security, a blueprint approach to its global security portfolio designed to deliver proactive advanced security for customers worldwide. Intelligent Security has been designed by Logicalis' worldwide team of security specialists to give customers the most comprehensive observability and protection available. It is based on tracking and analysing cyber threats and knowledge of the latest prevention methods deployed across its customer base. Logicalis' tenth annual CIO report surveyed 1,000 CIOs globally and found that of the 83% of CIOs who experienced cyber-attacks in the last 12 months, only 43% feel prepared for another breach. Designed to help CIOs manage these pressures, Intelligent Security will leverage Logicalis's security capabilities as well as its relationships with global partners such as Cisco and Microsoft, where it has the highest levels of security accreditations. Logicalis CTO Toby Alock said: "Many organisations focus on one area of the security fabric; we recognise in today's cyber security landscape there are no silos; organisations must look across the entire footprint, from secure connectivity, securing the cloud, securing the hybrid worker and weaving secure operations across the entire organisation". The Intelligent Security global portfolio delivers a concise and comprehensive proposition for customers, including: ● Advisory: Providing customers with a range of advisory services dedicated to helping them understand their security needs and identify the right solutions for their business. ● Secure workplace: Securing worker communications and devices as remote working continues to impact the workplace security landscape. ● Secure connectivity: Enforcing zero trust security from edge-to-edge, ensuring networks are robust enough to enable safe IoT, 5G and edge computing. ●  Secure hybrid cloud: Safeguarding cloud environments to ensure data, applications, and IT resources are safe. ● Secure Operations: Providing 24/7/365 best practice proactive threat detection and incident response, across three global security regions, using AI and automation to identify and triage. In addition to the Intelligent Security global portfolio, the company is announcing several key investments in cyber skills and capabilities to bolster its position as a global leader in security services. Expanding the Managed Security (SOC) team in Portugal, which currently serves customers across EMEA, including the UK, Ireland and Germany
Partnering with Cisco to develop a Cyber Academy and a pipeline of qualified cybersecurity graduates into the EMEA Security team
Partnering with local universities in Portugal to develop an Intern program, which helps develop cybersecurity skills to serve the EMEA region and beyond. Logicalis continues to prove its robust security offer by achieving Microsoft-verified Managed Extended Detection and Response (MXDR) Partner Status, launching Cisco Powered Intelligent Connectivity, built on zero-trust security, and being one of only six partners globally working with Cisco to develop an XDR proposition and enhance its Cisco security capabilities. Intelligent Security will be underpinned by Logicalis' Digital Fabric Platform, providing CIOs with deeper-level insights and recommendations to enhance the performance of their entire digital ecosystem. Toby Alock concludes, "We are at a pivotal point as security threats grow in scale and sophistication. CIOs and IT leaders are rethinking security in the era of AI and considering whether they have the in-house skills necessary to operate safely. Logicalis has seen a 300% surge in demand globally for Managed Security services in the past 12 months. Our customers are looking for a holistic, proactive approach to navigating security; we believe our simplified portfolio, our partnerships, and our investments in cyber talent for the next generation will deliver customers what they need: AI ready Managed Security with governance and SOC Protection wherever they are in the world". For more information please contact: Logicalis Team at Another Word Email: logicalis@anotherword.com Tel: 020 3176 0014 About Logicalis We are Architects of Change™. We help organisations succeed in a digital-first world. At Logicalis, we harness our collective technology expertise to help our clients build a blueprint for success, so they can deliver sustainable outcomes that matter. Our lifecycle services across cloud, connectivity, collaboration and security are designed to help optimise operations, reduce risk and empower employees. As a global technology service provider, we deliver next-generation digital managed services, to provide our clients with real-time visibility and actionable insights across the performance of their digital ecosystem including; availability, user experience, security, economic performance and sustainability. Our 7000+ ‘Architects of Change’ are based in 30 territories around the globe, helping our 10,000+ clients across a range of industry sectors create sustainable outcomes through technology. Logicalis has annualised revenues of $1.7 billion, from operations in Europe, North America, Latin America, Asia Pacific, and Africa. It is a division of Datatec Limited, listed on the Johannesburg Stock Exchange, with revenues of over $5.1 billion.\ Contact Katie Fraser ANOTHER WORD katie.fraser@anotherword.com 07908229152 This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. London, United Kingdom, May 13th, 2024, CyberNewsWire London, United Kingdom, May 13th, 2024, CyberNewsWire Logicalis, the global technology service provider delivering next-generation digital managed services, has today announced the launch of Intelligent Security, a blueprint approach to its global security portfolio designed to deliver proactive advanced security for customers worldwide. Intelligent Security has been designed by Logicalis' worldwide team of security specialists to give customers the most comprehensive observability and protection available. It is based on tracking and analysing cyber threats and knowledge of the latest prevention methods deployed across its customer base. Logicalis' tenth annual CIO report surveyed 1,000 CIOs globally and found that of the 83% of CIOs who experienced cyber-attacks in the last 12 months, only 43% feel prepared for another breach. Designed to help CIOs manage these pressures, Intelligent Security will leverage Logicalis's security capabilities as well as its relationships with global partners such as Cisco and Microsoft, where it has the highest levels of security accreditations. Logicalis CTO Toby Alock said: "Many organisations focus on one area of the security fabric; we recognise in today's cyber security landscape there are no silos; organisations must look across the entire footprint, from secure connectivity, securing the cloud, securing the hybrid worker and weaving secure operations across the entire organisation". "Many organisations focus on one area of the security fabric; we recognise in today's cyber security landscape there are no silos; organisations must look across the entire footprint, from secure connectivity, securing the cloud, securing the hybrid worker and weaving secure operations across the entire organisation". The Intelligent Security global portfolio delivers a concise and comprehensive proposition for customers, including: ● Advisory: Providing customers with a range of advisory services dedicated to helping them understand their security needs and identify the right solutions for their business. ● Secure workplace: Securing worker communications and devices as remote working continues to impact the workplace security landscape. ● Secure connectivity: Enforcing zero trust security from edge-to-edge, ensuring networks are robust enough to enable safe IoT, 5G and edge computing. ●  Secure hybrid cloud: Safeguarding cloud environments to ensure data, applications, and IT resources are safe. ● Secure Operations: Providing 24/7/365 best practice proactive threat detection and incident response, across three global security regions, using AI and automation to identify and triage. In addition to the Intelligent Security global portfolio, the company is announcing several key investments in cyber skills and capabilities to bolster its position as a global leader in security services. Expanding the Managed Security (SOC) team in Portugal, which currently serves customers across EMEA, including the UK, Ireland and Germany Partnering with Cisco to develop a Cyber Academy and a pipeline of qualified cybersecurity graduates into the EMEA Security team Partnering with local universities in Portugal to develop an Intern program, which helps develop cybersecurity skills to serve the EMEA region and beyond. Expanding the Managed Security (SOC) team in Portugal, which currently serves customers across EMEA, including the UK, Ireland and Germany Partnering with Cisco to develop a Cyber Academy and a pipeline of qualified cybersecurity graduates into the EMEA Security team Partnering with local universities in Portugal to develop an Intern program, which helps develop cybersecurity skills to serve the EMEA region and beyond. Logicalis continues to prove its robust security offer by achieving Microsoft-verified Managed Extended Detection and Response (MXDR) Partner Status, launching Cisco Powered Intelligent Connectivity, built on zero-trust security, and being one of only six partners globally working with Cisco to develop an XDR proposition and enhance its Cisco security capabilities. Intelligent Security will be underpinned by Logicalis' Digital Fabric Platform, providing CIOs with deeper-level insights and recommendations to enhance the performance of their entire digital ecosystem. Toby Alock concludes, "We are at a pivotal point as security threats grow in scale and sophistication. CIOs and IT leaders are rethinking security in the era of AI and considering whether they have the in-house skills necessary to operate safely. Logicalis has seen a 300% surge in demand globally for Managed Security services in the past 12 months. Our customers are looking for a holistic, proactive approach to navigating security; we believe our simplified portfolio, our partnerships, and our investments in cyber talent for the next generation will deliver customers what they need: AI ready Managed Security with governance and SOC Protection wherever they are in the world". Toby Alock concludes, "We are at a pivotal point as security threats grow in scale and sophistication. CIOs and IT leaders are rethinking security in the era of AI and considering whether they have the in-house skills necessary to operate safely. Logicalis has seen a 300% surge in demand globally for Managed Security services in the past 12 months. Our customers are looking for a holistic, proactive approach to navigating security; we believe our simplified portfolio, our partnerships, and our investments in cyber talent for the next generation will deliver customers what they need: AI ready Managed Security with governance and SOC Protection wherever they are in the world". For more information please contact : For more information please contact Logicalis Team at Another Word Email: logicalis@anotherword.com logicalis@anotherword.com logicalis@anotherword.com Tel: 020 3176 0014 About Logicalis We are Architects of Change™. We help organisations succeed in a digital-first world. At Logicalis, we harness our collective technology expertise to help our clients build a blueprint for success, so they can deliver sustainable outcomes that matter. Our lifecycle services across cloud, connectivity, collaboration and security are designed to help optimise operations, reduce risk and empower employees. As a global technology service provider, we deliver next-generation digital managed services, to provide our clients with real-time visibility and actionable insights across the performance of their digital ecosystem including; availability, user experience, security, economic performance and sustainability. Our 7000+ ‘Architects of Change’ are based in 30 territories around the globe, helping our 10,000+ clients across a range of industry sectors create sustainable outcomes through technology. Logicalis has annualised revenues of $1.7 billion, from operations in Europe, North America, Latin America, Asia Pacific, and Africa. It is a division of Datatec Limited, listed on the Johannesburg Stock Exchange, with revenues of over $5.1 billion.\ Contact Contact Katie Fraser ANOTHER WORD katie.fraser@anotherword.com 07908229152 This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Logicalis enhances global security services with the launch of Intelligent Security

We often hear about empathy as a singular concept—a soft skill, an essential quality of being human that connects us to others. But empathy comes in two flavors. It has shades, and understanding them might . make us better humans Two Sides of the Same Coin: Logical and Emotional Empathy This is the classic definition of empathy that most of us are familiar with. It’s feeling what another person feels. If your friend is sad, you feel sad. If they’re excited, you feel their joy. Emotional empathy happens almost instinctively. It’s raw and visceral. – Emotional Empathy: This form is more calculated. It’s understanding what another person feels without necessarily feeling it yourself. It’s more about perception, awareness, and insight. It’s about seeing things from their perspective, even if you don’t feel their viewpoint. – Logical Empathy: Emotional empathy might be more natural for some people. You know the type, those who can feel a room’s mood as tangibly as a physical touch. I’ve always admired that, but it wasn’t me. And then there are others for whom logical empathy might be more innate. These individuals are perceptive, analytical, and capable of seeing a situation from various angles without becoming emotionally entangled. Many of us who make our careers in technology are attracted to the industry because we have these skills. Learning the wrong lessons early Most of my first jobs were at large companies with very competitive and hierarchical cultures: IBM, Silicon Graphics, and Microsoft. was legendary for its’ hyper-competitive culture. I worked there for eight years. Microsoft taught me that I had to expect that other teams were constantly looking for how to undermine mine and that every outstretched hand was likely masking a knife held in the other hand behind the back. I eventually realized that the environment was a bad fit for me, but sadly, I didn’t get out until I had internalized those lessons. Microsoft in the 1990s After Microsoft, I sought out more collaborative environments, but I struggled. I constantly expected ill intent behind every action from a peer. I knew that this was hurting me and that I needed to move to a mindset of expecting positive intent from others, but I didn’t know how to rewire my brain. A Splash of Insight: David Foster Wallace’s “This is Water” My epiphany came when someone recommended that I read David Foster Wallace’s commencement address to Kenyon College, “ ” If you haven’t read or listened to it, it’s enlightening. Wallace talks about default settings, the unconscious, automatic ways we interpret everything around us. He speaks about learning to think more compassionately and understanding that everyone around you has a unique inner life full of dreams, fears, and struggles. And it’s not always about feeling their pain; sometimes, it’s about understanding their pain. This is Water. Wallace’s speech was a masterclass in logical empathy. And it gave me a better way to try and understand others’ intents, especially when you don’t know someone well. Developing Logical Empathy When Emotional Empathy Feels Unnatural So, how can you foster logical empathy if emotional empathy doesn’t come naturally to you? Here’s a roadmap: You don’t have to feel what someone else feels to understand them. Listen actively, engage with their words, and seek to understand their perspective. Listen More, Talk Less: If you don’t understand something, ask. Asking not only clarifies but demonstrates that you are engaged and interested in the other person’s perspective. Ask Questions: What could be the pressures on them that they may not be vocalizing? If you are talking to a salesperson near the end of the quota, could they be pressured to make their quota? Is the Product Manager being held to unrealistic expectations by their boss? Leverage what you know about the business or organization to understand what subtexts may be unsaid. Seek to Understand Their Context: Spend time thinking about the perspectives of others. Consider why they feel the way they do. Analyze their thoughts without judgment. Reflect: Try to visualize the scenario from their perspective. This mental exercise helps in understanding without feeling. Use Imagination: Logical empathy may not be instinctive, but it’s still a form of compassion. Approach situations and people with an open heart, even if it’s an analytical one. Practice Compassion: Embracing Both Forms The truth is logical, and are not mutually exclusive. You can be someone who mainly engages with logical empathy while still having the capacity for emotional empathy and vice versa. emotional empathy The real beauty lies in embracing both and recognizing that there’s no right or wrong way to connect with others. It’s a journey, and it’s one worth taking, regardless of where you naturally fall on the empathy spectrum. In our complex and diverse world, empathy in all its shades is more than a desirable trait; it’s a necessity. Understanding how you relate to others and working on enhancing that connection, be it through emotional or logical empathy, makes you not only a better colleague, friend, or partner but a more complete human being. This exploration of empathy, fostered by wise words from thinkers like David Foster Wallace, has been a personal awakening. It’s water, and now I see it. Also published . here

Logical Empathy: Lessons I Learned in Tech and How to Foster It

is one of the most revolutionary applications of blockchain technology. It's the
idea of turning real-world assets (RWAs) into digital representations that can live on
blockchains, opening a whole world of possibilities on how people interact and derive value
from those RWAs. Tokenization The bankruptcy claims market is, perhaps, the one space that needs this the most. An
estimated 20 million users are holding on to following the recent
wake of bankruptcies in the crypto space. And for a lot of them, tokenization of claims may
just be the respite they’ve been waiting for. claims worth $20 billion The messy business of bankruptcy claims When a company files for Chapter 11 bankruptcy, creditors can assert their right to payment
by filing a Proof of Claim with the bankruptcy court. The claim specifies the amount owed
and is usually the creditor’s only hope of getting their money back. But, having a bankruptcy claim doesn’t guarantee payment. This is because bankruptcy proceedings and restructuring financial affairs are often messy procedures. Proceedings may take years to conclude due to administrative obstacles, legal problems, and complex financial issues. And at the end of it all, not all claims are treated equally. Bankruptcy claims are assigned different priority levels. There are high-priority claims, which
must be repaid in full. Holders of other claims have to wait until these claims are sorted out.
This means lower-priority claims often don’t get paid in full or even end up totally unpaid. Thus, many creditors have grown weary of the bankruptcy process. This has led to the
development of an OTC aftermarket for bankruptcy claims. Here, creditors sell their bankruptcy claims to investors for cash, usually at a lower price than
the value of the claim. The investor hopes to make a profit down the road when the
bankruptcy proceedings conclude. Bankruptcy claims trading typically takes place with the help of bankruptcy lawyers or
agency brokers. And while it has certainly helped creditors derive some value from their
claims, it has its own share of problems. Problem with traditional claims trading Today, people who want to buy or sell bankruptcy claims can only do it via bankruptcy lawyers or brokers. However, both options can be expensive – lawyers demand high legal fees while agency brokers charge a significant portion of the transaction cost as commission. Therefore, they’re typically only accessible to larger claim holders. The market also suffers from an overall lack of transparency and information asymmetry.
Buyers don’t have convenient access to the relevant information about claims. This leaves
them vulnerable to vulture pricing. Both buyers and sellers are also often victims of payment
fraud and impersonation scams. Thus, they often have to engage the services of financial advisors or bankruptcy attorneys to
help assess the risks and opportunities of assets, which further increases the costs of claims
trading. Traditionally, the solution to all these problems was to have a whole lot of money. But this is
not something all claim holders and potential investors have. This makes it difficult for
investors to buy or sell claims, leading to another huge problem – lack of liquidity in the
bankruptcy claims market. Fortunately, blockchain technology has a better solution – tokenization. How tokenization changes things When RWAs are tokenized, they have the same financial value as the underlying assets,
with the added feature of being as tradeable as cryptocurrencies. And this is what will
revolutionize bankruptcy claims trading. Take fungibility for instance. Fungibility is the ability to exchange one asset with another of the same type without affecting its value. With tokenization, a bankruptcy claim can be converted into many digital tokens, each representing a fraction of the claim’s value. These tokens can then be freely traded on a crypto exchange with other tokens or even fiat currency. Do that for multiple bankruptcy claims and you end up with a massive pool of fungible
tokens. These can be easily traded without needing lawyers, investment advisors, or agency
brokers. This leads to a greatly reduced cost of investment. Tokenization also greatly increases the level of transparency. When claims are pooled and
made tradeable on an exchange, there will be more price transparency, leading to better
pricing. All these features make bankruptcy trading a more attractive and accessible option. It opens
up the market to both small and large claim traders. And, with more investors comes more
liquidity, which, in turn, makes trades cheaper and more accessible. Is tokenizing bankruptcy claims the future? With billions of dollars locked up in bankruptcy claims and no relief in sight, tokenization may
just be what the crypto market needs. Fortunately, this concept isn’t just theoretical
speculation. There’s a crypto company looking to tokenize claims trading. Called Open Exchange
(OPNX), the company has built an order book exchange that lets people buy and sell
tokenized claims (as well as crypto), starting with Celsius and soon, FTX. The company has standardized claims into tokens that can then be freely traded or used as collateral to trade
crypto futures. More similar solutions will likely emerge to tokenize RWAs, like claims. This means that the
next couple of years will be an interesting time in the bankruptcy claims market as
tokenization opens new avenues for trading for more people than ever before.

Tokenization: the Next Logical Step in Bankruptcy Claims Trading

Introduction: Ever since the buzz about GenAI and ChatGPT, I have been trying to understand the space and leveraging it into my day-to-day activities as a Data Engineer. No doubt it has been useful for debugging with respective programming and documentation. Personally, it played a significant role in helping me plan my Dubai itinerary. It even contributed to naming my friend’s daughter. However, it works pretty well if you give appropriate and precise requirements. There is a lot of anxiety among people that AI is going to take our jobs as they feel almost everything will be automated through AI. Is LLM as good as everyone claims? In this article, we will look into the specifics of Gen AI’s role in data engineering and see where it flourishes, where it requires enhancement, and where human expertise remains irreplaceable. Areas of Strength: Basic Data Querying: 100% the human necessity is going to get disrupted, but the severity based on the complexity is high. Even the SQL practitioners won’t feel much difference here, and self-serve analytics will catch fire in this area which is something phenomenal! Again, to quote positively, data engineers won’t need to guide the stakeholders nearly as much! Troubleshooting Pipeline Failures: LLMs coupled with agents will slash on-call load massively. LLMs processing stack traces/quality failures can recommend the built-in remedies and nudge the ball down the field to address the problem, presumably even over Slack. And that is a fantastic percentage of data engineering hours saved! To be clear, some hard failures require manual troubleshooting. Anomaly Detection: Regardless of the structure, format, source, complexity, timeliness, or even natural play of data, large language models can identify anomalies. LLMs are well-suited to anomaly identification because they can work across a variety of data sources, identify subtle patterns, and distinguish when data isn’t behaving as expected. This will help data engineers to identify and fix data quality problems in advance. Areas for Enhancement: Data Cleansing: Our results suggest that Language Models (LLMs) are highly effective in data cleansing due to their high performance in unstructured data management and semantic error identification and correction, as well as high scalability. Furthermore, our review of the current state-of-the-art suggests that even more substantial improvements are possible, including fine-tuning of LLMs to specific domains or even organization data. In addition, various interactive mechanisms as well as coupling with data quality frameworks can significantly increase the efficiency of LLMs. Overall, LLMs allow for organizing and improving the whole process of data cleansing, helping to obtain high-quality datasets, and, consequently, valuable insights. Visualization and Report Generation: Language Models (LLMs) excel in visualization and documentation tasks by automating the creation of reports, summarizing data insights, and generating narrative-driven reports. Enhancements needed include improving document organization and categorization, data-driven storytelling capabilities integrating with version control systems and visualization tools for seamless output generation, and enhancing interpretability features to ensure clarity and coherence in reports. Writing Pipeline Code in SQL, Spark, or Flink LLM can be a helpful starting point, and for our work, it is a good boilerplate. Still, the generated material is hard to debug — or often not, worth the trouble. Prompt-driven content is still far from being able to turn out a nice, optimized, and absolutely accurate data pipeline. Optimization By providing suggestions on efficient query structures and detecting problems that slow performance, LLMs are especially powerful when it comes to SQL optimization. They can even make recommendations for optimizations on the basis of historical query data. Things for improvement include integrating database-specific optimization techniques into the training of LLM, bridging with query execution plans in order to give context-oriented advice and feedback mechanisms so that one can modify strategies for optimization according to what users prefer as well as performance metrics. Physical Data Model As soon as the logical model is finalized, LLM will be efficient in moving and upgrading this logical (or conceptual) data model into the targeted architecture. The problem that LLM encounters with all the examples is that it’s hard for most people to understand the pipeline, table, and its relationship. Areas Where Human Expertise Remains Irreplaceable: Logical Data Model Data modeling is a very soft skills-focused task. You have to communicate with different stakeholders to understand the business context in detail. Understanding what data is needed, the relationship between entities, and considering different scenarios are human-oriented tasks that LLMs must continue to struggle with. Building Pipeline and Framework Once they reach a certain level of complexity, LLMs begin to give up. Imagine if someone asked ChatGPT, “Can you provide me the source code of Android’s next version?” While LLMs can help with routine data engineering tasks, they do not have the insight or inventiveness unique to humans that allows for new solutions to complex challenges, unlocking creativity in technical terms. Domain Expertise and Intuition: Human data engineers bring a unique blend of domain expertise and intuition to their work that often surpasses what conventional software isn’t able to achieve through automation. This intuition allows them to find subtle patterns in data that no fully rigorous automated system can. Based on their knowledge of this field and their experiences in it, they can predict future trends. They can also make decisions in light of a broader context, including corporate or government needs, ethical considerations, and potential future scenarios. This holistic approach to data engineering surpasses what LLMs have achieved. Although LLMs have great power, they are typically restricted to the patterns and relationships they have been trained to recognize. Conclusion: On the whole, Gen AI can certainly automate trivial procedures such as looking for information, launching programs that don’t work properly, repeatedly correcting faults, and anomaly detection. My gut tells me that Gen AI will win out and excel at tasks such as Data Cleansing, Optimization, and Writing Pipelines — once the team understands gaps and missing patterns. But it can not learn purely human soft skills that are important for building logical data models or a framework. I believe that these skills, which cannot be automated, are also essential for stakeholders to ask for innovation in the first place. After all, no one ever asked Steve Jobs if they needed anything like an iPhone; he just thought up what would become its invention. References: https://barrmoses.medium.com/will-genai-replace-data-engineers-no-and-heres-why-708b0a27da6b ​​__https://www.linkedin.com/pulse/navigating-data-governance-challenges-era-large-fnq5e__ https://www.dataversity.net/navigating-the-risks-of-llm-ai-tools-for-data-governance/ https://www.skyflow.com/post/why-ai-governance-requires-data-governance https://coreykeyser.medium.com/you-cant-govern-ai-without-governing-data-27bb88d8f9ea https://www.ankursnewsletter.com/p/gpt-4-gpt-3-and-gpt-35-turbo-a-review https://towardsdatascience.com/from-chaos-to-clarity-streamlining-data-cleansing-using-large-language-models-a539fa0b2d90 https://medium.com/@wangshally11/data-cleaning-warm-up-before-training-large-language-models-74ed1edceead Introduction: Ever since the buzz about GenAI and ChatGPT, I have been trying to understand the space and leveraging it into my day-to-day activities as a Data Engineer. No doubt it has been useful for debugging with respective programming and documentation. Personally, it played a significant role in helping me plan my Dubai itinerary. It even contributed to naming my friend’s daughter. However, it works pretty well if you give appropriate and precise requirements. There is a lot of anxiety among people that AI is going to take our jobs as they feel almost everything will be automated through AI. Is LLM as good as everyone claims? In this article, we will look into the specifics of Gen AI’s role in data engineering and see where it flourishes, where it requires enhancement, and where human expertise remains irreplaceable. Areas of Strength: Basic Data Querying: 100% the human necessity is going to get disrupted, but the severity based on the complexity is high. Even the SQL practitioners won’t feel much difference here, and self-serve analytics will catch fire in this area which is something phenomenal! Again, to quote positively, data engineers won’t need to guide the stakeholders nearly as much! Troubleshooting Pipeline Failures: LLMs coupled with agents will slash on-call load massively. LLMs processing stack traces/quality failures can recommend the built-in remedies and nudge the ball down the field to address the problem, presumably even over Slack. And that is a fantastic percentage of data engineering hours saved! To be clear, some hard failures require manual troubleshooting. Anomaly Detection: Regardless of the structure, format, source, complexity, timeliness, or even natural play of data, large language models can identify anomalies. LLMs are well-suited to anomaly identification because they can work across a variety of data sources, identify subtle patterns, and distinguish when data isn’t behaving as expected. This will help data engineers to identify and fix data quality problems in advance. Areas for Enhancement: Data Cleansing: Our results suggest that Language Models (LLMs) are highly effective in data cleansing due to their high performance in unstructured data management and semantic error identification and correction, as well as high scalability. Furthermore, our review of the current state-of-the-art suggests that even more substantial improvements are possible, including fine-tuning of LLMs to specific domains or even organization data. In addition, various interactive mechanisms as well as coupling with data quality frameworks can significantly increase the efficiency of LLMs. Overall, LLMs allow for organizing and improving the whole process of data cleansing, helping to obtain high-quality datasets, and, consequently, valuable insights. Visualization and Report Generation: Language Models (LLMs) excel in visualization and documentation tasks by automating the creation of reports, summarizing data insights, and generating narrative-driven reports. Enhancements needed include improving document organization and categorization, data-driven storytelling capabilities integrating with version control systems and visualization tools for seamless output generation, and enhancing interpretability features to ensure clarity and coherence in reports. Writing Pipeline Code in SQL, Spark, or Flink LLM can be a helpful starting point, and for our work, it is a good boilerplate. Still, the generated material is hard to debug — or often not, worth the trouble. Prompt-driven content is still far from being able to turn out a nice, optimized, and absolutely accurate data pipeline. Optimization By providing suggestions on efficient query structures and detecting problems that slow performance, LLMs are especially powerful when it comes to SQL optimization. They can even make recommendations for optimizations on the basis of historical query data. Things for improvement include integrating database-specific optimization techniques into the training of LLM, bridging with query execution plans in order to give context-oriented advice and feedback mechanisms so that one can modify strategies for optimization according to what users prefer as well as performance metrics. Physical Data Model As soon as the logical model is finalized, LLM will be efficient in moving and upgrading this logical (or conceptual) data model into the targeted architecture. The problem that LLM encounters with all the examples is that it’s hard for most people to understand the pipeline, table, and its relationship. Areas Where Human Expertise Remains Irreplaceable: Logical Data Model Data modeling is a very soft skills-focused task. You have to communicate with different stakeholders to understand the business context in detail. Understanding what data is needed, the relationship between entities, and considering different scenarios are human-oriented tasks that LLMs must continue to struggle with. Building Pipeline and Framework Once they reach a certain level of complexity, LLMs begin to give up. Imagine if someone asked ChatGPT, “Can you provide me the source code of Android’s next version?” While LLMs can help with routine data engineering tasks, they do not have the insight or inventiveness unique to humans that allows for new solutions to complex challenges, unlocking creativity in technical terms. Domain Expertise and Intuition: Human data engineers bring a unique blend of domain expertise and intuition to their work that often surpasses what conventional software isn’t able to achieve through automation. This intuition allows them to find subtle patterns in data that no fully rigorous automated system can. Based on their knowledge of this field and their experiences in it, they can predict future trends. They can also make decisions in light of a broader context, including corporate or government needs, ethical considerations, and potential future scenarios. This holistic approach to data engineering surpasses what LLMs have achieved. Although LLMs have great power, they are typically restricted to the patterns and relationships they have been trained to recognize. Conclusion: On the whole, Gen AI can certainly automate trivial procedures such as looking for information, launching programs that don’t work properly, repeatedly correcting faults, and anomaly detection. My gut tells me that Gen AI will win out and excel at tasks such as Data Cleansing, Optimization, and Writing Pipelines — once the team understands gaps and missing patterns. But it can not learn purely human soft skills that are important for building logical data models or a framework. I believe that these skills, which cannot be automated, are also essential for stakeholders to ask for innovation in the first place. After all, no one ever asked Steve Jobs if they needed anything like an iPhone; he just thought up what would become its invention. References: https://barrmoses.medium.com/will-genai-replace-data-engineers-no-and-heres-why-708b0a27da6b https://barrmoses.medium.com/will-genai-replace-data-engineers-no-and-heres-why-708b0a27da6b https://barrmoses.medium.com/will-genai-replace-data-engineers-no-and-heres-why-708b0a27da6b ​​__ https://www.linkedin.com/pulse/navigating-data-governance-challenges-era-large-fnq5e __ https://www.linkedin.com/pulse/navigating-data-governance-challenges-era-large-fnq5e https://www.dataversity.net/navigating-the-risks-of-llm-ai-tools-for-data-governance/ https://www.dataversity.net/navigating-the-risks-of-llm-ai-tools-for-data-governance/ https://www.dataversity.net/navigating-the-risks-of-llm-ai-tools-for-data-governance/ https://www.skyflow.com/post/why-ai-governance-requires-data-governance https://www.skyflow.com/post/why-ai-governance-requires-data-governance https://www.skyflow.com/post/why-ai-governance-requires-data-governance https://coreykeyser.medium.com/you-cant-govern-ai-without-governing-data-27bb88d8f9ea https://coreykeyser.medium.com/you-cant-govern-ai-without-governing-data-27bb88d8f9ea https://coreykeyser.medium.com/you-cant-govern-ai-without-governing-data-27bb88d8f9ea https://www.ankursnewsletter.com/p/gpt-4-gpt-3-and-gpt-35-turbo-a-review https://www.ankursnewsletter.com/p/gpt-4-gpt-3-and-gpt-35-turbo-a-review https://www.ankursnewsletter.com/p/gpt-4-gpt-3-and-gpt-35-turbo-a-review https://towardsdatascience.com/from-chaos-to-clarity-streamlining-data-cleansing-using-large-language-models-a539fa0b2d90 https://towardsdatascience.com/from-chaos-to-clarity-streamlining-data-cleansing-using-large-language-models-a539fa0b2d90 https://towardsdatascience.com/from-chaos-to-clarity-streamlining-data-cleansing-using-large-language-models-a539fa0b2d90 https://medium.com/@wangshally11/data-cleaning-warm-up-before-training-large-language-models-74ed1edceead https://medium.com/@wangshally11/data-cleaning-warm-up-before-training-large-language-models-74ed1edceead https://medium.com/@wangshally11/data-cleaning-warm-up-before-training-large-language-models-74ed1edceead

LLMs: An Assessment From a Data Engineer

More than 300 exabytes of data are created every day. To put that number into perspective, one exabyte equals one billion gigabytes. Considering the amount of data being created, there is a need for more efficient and easier data management and analytics. The recent proliferation of AI in people's personal and professional lives explains why they would look at AI to address their data management challenges. In fact, 64% of business owners believe AI will help them increase productivity. Data mapping, a crucial step in data management, is ideal for automation. It's a repetitive and time-consuming process, making it well-suited for AI. Automating data mapping can free up time for higher-priority tasks. This post looks at data mapping and its different types, and the role of data mapping in data management. It will also discuss how the combination of AI and data mapping makes data management easy and accessible and presents opportunities for organizations and software vendors alike. What is Data Mapping? Data mapping connects and transforms data to be shared from one system or database to another. Consider this: A business wants to launch a referral reward program. It has a database holding all its customer data and wants to connect it to a database holding purchase records. Mapping the relevant data from the source to the newly merged database lets it do just that. With the databases properly mapped, the business will now have access to actionable information in real time, letting them know which customers to contact for the program. In other words, data mapping helps different databases connect and communicate. This can improve business intelligence capabilities throughout the organization. It also allows core functions like Marketing, Product Development, and Finance to operate synergistically, using real-time data to drive their decision-making. Types of Data Mapping Data mapping has three main types: manual, semi-automated, and automated. The manual approach to data mapping has been the norm for decades. However, semi-automated and automated data mapping approaches are quickly gaining ground due to their benefits. Let’s look at some of these benefits: Manual Data Mapping
Manual mapping offers users greater flexibility and control over their data flow. However, it can be a long and repetitive process. If done using coding languages like SQL or C++, it requires a lot of working hours and technical expertise. Manual data mapping is no longer a practical option, considering the amount of data that organizations create every minute. Semi-Automated Data Mapping
The semi-automated approach offers a good balance between flexibility and efficiency. A good example of semi-automated data mapping is schema maps. Semi-automated data mappers use visual representations of the data maps, allowing users with limited technical knowledge to construct dataflows. This approach is less time-intensive and doesn’t require much coding expertise. However, it still needs some technical knowledge, regardless of whether an organization relies on coding or drag-and-drop solutions. Automated Data Mapping Automated data mapping offers many benefits compared to manual and semi-automated data mapping. For instance, it offers flexibility and speed and doesn’t require technical knowledge. The automated approach makes scheduling, scaling, and deploying your dataflows easy. However, the necessary technical training will be platform-specific and not necessarily transferable. The Importance of Data Mapping As businesses collect more data, data mapping becomes increasingly essential. This scenario is even more challenging because businesses gather data from multiple sources. That data becomes unusable if they fail to collect, organize, clean, and transform it into actionable insights properly and on time. In other words, proper data mapping is like implementing a filing system to ensure that all your data ends up in the right place, in the suitable format, at the right time. With this filing system, your organization will catch up in data management, analytics, and business intelligence functions. AI and Data Mapping The trend of AI adoption in data management relates to the evolving data needs of organizations. With efficiency and ease of use as priorities, AI’s role becomes even more important in data management, specifically data mapping. For instance, semi-automated data mapping solutions were present well before the AI boom. So, the need to automate the data mapping process is not new. Even so, the AI wave has the potential to make fully automated data mapping the standard practice in data management. With AI getting tons of attention and resources, the time is right for organizations and software vendors to solve data management challenges using AI. While AI is already being implemented across data management tools, full data mapping automation is still a work in progress. Nevertheless, the following are some ways AI is democratizing data management by transforming the data mapping process: Automated Discovery of Data Sources and Attributes: AI-powered data mappers automate the discovery of relevant data sources, attributes, and possible connections. This allows users to eliminate the lengthy steps of manual data mapping. Users can fine-tweak the AI-generated maps to their specific needs and accept or reject AI suggestions as they see fit. This significantly simplifies data mapping, letting users handle complex dataflows in relatively shorter times.


Contextual Analysis to Identify Patterns and Connections: What sets AI apart from generic automation tools is its ability to think and analyze. An example is interaction with large language models (LLMs) like ChatGPT or Gemini. These LLMs can understand the context and tailor their responses accordingly. Similarly, AI-powered data mapping solutions can analyze and interpret the data fields to intelligently identify and establish patterns and relationships. This significantly reduces the technical knowledge requirement, allowing even beginners to use data management solutions with little expertise.


Simplified Schema Mapping: Data mapping algorithms also use pattern recognition to identify semantic relationships. This simplifies schema mapping as AI can find similarities between seemingly unrelated schemas. In short, AI-assisted field and schema mapping ensure smooth data integration, reducing complexity for end-users. AI and Data Mapping: Why It’s a Good Combination Data mapping is a crucial step, but it can get repetitive and taxing quickly. By incorporating AI, data management platforms can streamline, speed up, and simplify the entire process for their end-users. Moreover, by cutting the requirement for technical knowledge, software vendors and organizations alike can promote data literacy among their internal and external stakeholders. As a result, organizations can promote a more efficient and effective use of data across their functions. For instance, marketers without coding experience can gather actionable campaign insights. Similarly, the Research and Development team can leverage data from Customer Support to improve their offerings. Similarly, software vendors can cater to a larger customer base by customizing their offerings for non-technical users. They can empower their customers to move beyond the traditional approach, democratizing data management across all relevant functions. This win-win proposition should encourage AI adoption for data mapping among all stakeholders. Conclusion Combining data mapping and AI is a logical step to meet the evolving needs of data management for all stakeholders. For businesses, it democratizes data management, simplifies the data mapping process, and saves time and costs. For software vendors, this integration provides access to a larger audience, increasing profitability and reach. For end users, the AI and data mapping combination makes their day-to-day work easier, faster, and more effective. More than 300 exabytes of data are created every day. To put that number into perspective, one exabyte equals one billion gigabytes. 300 exabytes of data Considering the amount of data being created, there is a need for more efficient and easier data management and analytics. The recent proliferation of AI in people's personal and professional lives explains why they would look at AI to address their data management challenges. In fact, 64% of business owners believe AI will help them increase productivity. 64% of business owners Data mapping , a crucial step in data management, is ideal for automation. It's a repetitive and time-consuming process, making it well-suited for AI. Automating data mapping can free up time for higher-priority tasks. Data mapping This post looks at data mapping and its different types, and the role of data mapping in data management. It will also discuss how the combination of AI and data mapping makes data management easy and accessible and presents opportunities for organizations and software vendors alike. What is Data Mapping? Data mapping connects and transforms data to be shared from one system or database to another. Consider this: A business wants to launch a referral reward program. It has a database holding all its customer data and wants to connect it to a database holding purchase records. Mapping the relevant data from the source to the newly merged database lets it do just that. With the databases properly mapped, the business will now have access to actionable information in real time, letting them know which customers to contact for the program. In other words, data mapping helps different databases connect and communicate. This can improve business intelligence capabilities throughout the organization. It also allows core functions like Marketing, Product Development, and Finance to operate synergistically, using real-time data to drive their decision-making. Types of Data Mapping Data mapping has three main types: manual, semi-automated, and automated. The manual approach to data mapping has been the norm for decades. However, semi-automated and automated data mapping approaches are quickly gaining ground due to their benefits. Let’s look at some of these benefits: Manual Data Mapping Manual mapping offers users greater flexibility and control over their data flow. However, it can be a long and repetitive process. If done using coding languages like SQL or C++, it requires a lot of working hours and technical expertise. Manual data mapping is no longer a practical option, considering the amount of data that organizations create every minute. Manual Data Mapping Semi-Automated Data Mapping The semi-automated approach offers a good balance between flexibility and efficiency. A good example of semi-automated data mapping is schema maps. Semi-automated data mappers use visual representations of the data maps, allowing users with limited technical knowledge to construct dataflows. This approach is less time-intensive and doesn’t require much coding expertise. However, it still needs some technical knowledge, regardless of whether an organization relies on coding or drag-and-drop solutions. Semi-Automated Data Mapping Automated Data Mapping Automated Data Mapping Automated data mapping offers many benefits compared to manual and semi-automated data mapping. For instance, it offers flexibility and speed and doesn’t require technical knowledge. The automated approach makes scheduling, scaling, and deploying your dataflows easy. However, the necessary technical training will be platform-specific and not necessarily transferable. The Importance of Data Mapping As businesses collect more data, data mapping becomes increasingly essential. This scenario is even more challenging because businesses gather data from multiple sources. That data becomes unusable if they fail to collect, organize, clean, and transform it into actionable insights properly and on time. In other words, proper data mapping is like implementing a filing system to ensure that all your data ends up in the right place, in the suitable format, at the right time. With this filing system, your organization will catch up in data management, analytics, and business intelligence functions. AI and Data Mapping The trend of AI adoption in data management relates to the evolving data needs of organizations. With efficiency and ease of use as priorities, AI’s role becomes even more important in data management, specifically data mapping. For instance, semi-automated data mapping solutions were present well before the AI boom. So, the need to automate the data mapping process is not new. Even so, the AI wave has the potential to make fully automated data mapping the standard practice in data management. With AI getting tons of attention and resources, the time is right for organizations and software vendors to solve data management challenges using AI. While AI is already being implemented across data management tools, full data mapping automation is still a work in progress. Nevertheless, the following are some ways AI is democratizing data management by transforming the data mapping process: Automated Discovery of Data Sources and Attributes: AI-powered data mappers automate the discovery of relevant data sources, attributes, and possible connections. This allows users to eliminate the lengthy steps of manual data mapping. Users can fine-tweak the AI-generated maps to their specific needs and accept or reject AI suggestions as they see fit. This significantly simplifies data mapping, letting users handle complex dataflows in relatively shorter times. Contextual Analysis to Identify Patterns and Connections: What sets AI apart from generic automation tools is its ability to think and analyze. An example is interaction with large language models (LLMs) like ChatGPT or Gemini. These LLMs can understand the context and tailor their responses accordingly. Similarly, AI-powered data mapping solutions can analyze and interpret the data fields to intelligently identify and establish patterns and relationships. This significantly reduces the technical knowledge requirement, allowing even beginners to use data management solutions with little expertise. Simplified Schema Mapping: Data mapping algorithms also use pattern recognition to identify semantic relationships. This simplifies schema mapping as AI can find similarities between seemingly unrelated schemas. In short, AI-assisted field and schema mapping ensure smooth data integration, reducing complexity for end-users. Automated Discovery of Data Sources and Attributes: AI-powered data mappers automate the discovery of relevant data sources, attributes, and possible connections. This allows users to eliminate the lengthy steps of manual data mapping. Users can fine-tweak the AI-generated maps to their specific needs and accept or reject AI suggestions as they see fit. This significantly simplifies data mapping, letting users handle complex dataflows in relatively shorter times. Automated Discovery of Data Sources and Attributes: AI-powered data mappers automate the discovery of relevant data sources, attributes, and possible connections. This allows users to eliminate the lengthy steps of manual data mapping. Users can fine-tweak the AI-generated maps to their specific needs and accept or reject AI suggestions as they see fit. This significantly simplifies data mapping, letting users handle complex dataflows in relatively shorter times. Automated Discovery of Data Sources and Attributes: Contextual Analysis to Identify Patterns and Connections: What sets AI apart from generic automation tools is its ability to think and analyze. An example is interaction with large language models (LLMs) like ChatGPT or Gemini. These LLMs can understand the context and tailor their responses accordingly. Similarly, AI-powered data mapping solutions can analyze and interpret the data fields to intelligently identify and establish patterns and relationships. This significantly reduces the technical knowledge requirement, allowing even beginners to use data management solutions with little expertise. Contextual Analysis to Identify Patterns and Connections: What sets AI apart from generic automation tools is its ability to think and analyze. An example is interaction with large language models (LLMs) like ChatGPT or Gemini. These LLMs can understand the context and tailor their responses accordingly. Similarly, AI-powered data mapping solutions can analyze and interpret the data fields to intelligently identify and establish patterns and relationships. This significantly reduces the technical knowledge requirement, allowing even beginners to use data management solutions with little expertise. Contextual Analysis to Identify Patterns and Connections: think analyze. Simplified Schema Mapping: Data mapping algorithms also use pattern recognition to identify semantic relationships. This simplifies schema mapping as AI can find similarities between seemingly unrelated schemas. In short, AI-assisted field and schema mapping ensure smooth data integration, reducing complexity for end-users. Simplified Schema Mapping: Data mapping algorithms also use pattern recognition to identify semantic relationships. This simplifies schema mapping as AI can find similarities between seemingly unrelated schemas. In short, AI-assisted field and schema mapping ensure smooth data integration, reducing complexity for end-users. Simplified Schema Mapping: AI and Data Mapping: Why It’s a Good Combination Data mapping is a crucial step, but it can get repetitive and taxing quickly. By incorporating AI, data management platforms can streamline, speed up, and simplify the entire process for their end-users. Moreover, by cutting the requirement for technical knowledge, software vendors and organizations alike can promote data literacy among their internal and external stakeholders. As a result, organizations can promote a more efficient and effective use of data across their functions. For instance, marketers without coding experience can gather actionable campaign insights. Similarly, the Research and Development team can leverage data from Customer Support to improve their offerings. Similarly, software vendors can cater to a larger customer base by customizing their offerings for non-technical users. They can empower their customers to move beyond the traditional approach, democratizing data management across all relevant functions. This win-win proposition should encourage AI adoption for data mapping among all stakeholders. Conclusion Combining data mapping and AI is a logical step to meet the evolving needs of data management for all stakeholders. For businesses, it democratizes data management, simplifies the data mapping process, and saves time and costs. For software vendors, this integration provides access to a larger audience, increasing profitability and reach. For end users, the AI and data mapping combination makes their day-to-day work easier, faster, and more effective.

How AI-Powered Data Mapping is Democratizing Data Management 

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