Glance is one of the world’s simplest, most reliable, and secure solutions that empowers enterprises to see, show, and share anything with digitally connected customers

Glance Networks

Several kinds of research and implementations have led to the discovery of numerous best-performing artificial intelligence systems in the past years. The idea behind deep learning was to amplify and replicate the brain’s structure and a function called artificial neural networks. Nowadays, every other individual is familiar with artificial intelligence, but few know that deep learning is basically a new name for Neural networks. Neural networks are older than most people think, and the idea was first Warren McCullough and Walter Pitts in 1944. Since then, the technology has been constantly tested and improved for now. proposed by 77 years Both of the Neural networks’ inventors were students of the and moved to in 1952 as the founding members of the first-ever “Cognitive Science Department.” University of Chicago MIT At first glance, Neural networks were being considered a very innovative and viable idea, and major researches in the neuroscience and computer science field were observed until 1969. When MIT mathematicians and became the co-director of the new MIT Artificial intelligence lab, the research was stopped. Hence, the technology was in a resting state until the 1980s when Neural networks were reborn. However, the technology then again fell into eclipse. Marvin Minsky Seymour Papert It was the dawn of computers that helped bring the technology back again, and this time it was fueled by the processing power of graphic chips. After its resurgence, Neural networks became the talk of the town. Liquid Neural Network Technology is Born After constant improvements and testing, This technology is capable of adapting its underlying behavior the training phase. a new type of Neural network technology was born. during The new term given to this technology was “Liquid” Neural networking technology devised by MIT Computer science and artificial intelligence Lab’s and his team. Ramin Hasani Ramin Hasani’s team’s efforts had turned the tides and bought us several revolutionary technologies like autonomous driving, robot control, and enhanced medical diagnostics. This new technology became the leading cause of several innovations due to its sophisticated analytical algorithms and innovative analytical abilities. After the technology training phase, the Neural network algorithms are provided with a large volume of relevant data that they analyze, understand, and store to hone their inference capabilities and optimize these devices’ performance. However, the “Liquid” Neural network could analyze and adapt to the success metrics over time and constantly harness new information to increase performance. The small number of neurons within the technology made it easier to understand and communicate information, which led to better decision making and performance. The main difference in the methodology introduced by Hasani and his team was that it focused on time-series adaptability. This means that rather than analyzing and understanding data based on a few numbers of images or static moments, the “Liquid” Neural technology could consider time-series data or sequences of images to better cultivate the information. The system’s design also enables researchers to further study and observe the network compared to traditional Neural networks. This kind of AI was generally called the “Black box.” The term “Black box” was given because researchers who were developing the algorithm knew the best way to input information and the criteria for determining a successful outcome. However, they could not identify the patterns within the Neural network to determine what was leading it to success. The “Liquid” Neural network model, on the other hand, gave the researchers more transparency, and it was also affordable when it came to implementing computing because it relied on fewer but more sophisticated computing nodes. There were literally many reasons why this “Liquid” Neural network technology was being considered viable in the future. The performance results derived from the technology also indicated that the “Liquid” Neural network offered better accuracy than its alternatives, and it could be used to more efficiently predict the future values of known data sets. How Neural Networks Work Today Let’s analyze how Neural networks or neural nets work today after being modernized for so many years. The Neural nets are basically computer programs that are assembled from millions of units. These units were designed to work as an artificial neuron. Above, we spoke about “Training a Neural network.” This training is linked to data integration. When a Neural network is being trained, it is actually fed information. This feeding allows it to recognize structures and patterns. Some examples of these recognitions can be spotting a familiar face in photos or identifying how to hit a ball. Moreover, with feedback and a constant interchanging of information, neural networks further modify their performance and information processing. This network actually learns over time and improves its performance. When training is complete, the Neural sets are all set to solve numerous problems instantly and effectively. They can notice deviations in historical patterns and automatically recognize trigger points in patterns, which generally means that they can identify, analyze and solve complex problems. The Key Strengths Of “Liquid” Neural Networks It is usually easy for a machine to follow a strict set of rules and regulations to solve a problem or even decipher a code. Instant Problem Identification & Solving However, when speech patterns and medical diagnostics need identification or solving, more variables are required to do the job. The machines need to understand what they’re looking for and how they can distinguish from other similar results that can alter the final outcome. And this part is exactly where “Liquid” Neural Networks come into play. This network is amazingly good at solving big problems, and nowadays, they’ve surpassed even humans in some aspects. The Feature Engineering Advantage Feature engineering is the process of Neural networks to analyze and create more possibilities, categories, and considerations that allow the user to understand what more needs to be done to ensure success. For example, if you’re training or teaching a machine to learn how to play Flappy Bird, you might have to teach it an algorithm so that it could learn how to play it. However, there is generally very little to no chance of ensuring that every tap on the phone by a machine would ensure successful passage through the given spaces. To better understand and cope with such conditions, the “Liquid” Neural networking technology gives you more categories that you need to focus on to program the machine to win at all costs. The Power of Flexibility The power of flexibility is that neural networks can be applied to almost every technology today. The seamless integration of this technology enables people to leverage it in any way they deem necessary. Whether you want this technology to solve a Rubik’s cube or you want it to recognize patterns or improve air traffic patterns to efficiently manage air traffic control – Neural nets can seamlessly recognize and identify patterns in almost any domain. What is the Future of Neural Networks? The “liquid” Neural network has an extremely promising future. What seemed like a dream has now become a reality. This technology has become one of the most important programming paradigms ever invented. Gone are the times when programmers used to tell the computers about the problems. The conventional approach of precisely defining a task to the computer so that it could solve it has been minimized by “Liquid” Neural Network. We’re constantly driving towards a future where a computer does not require precise information to solve our problems. Instead, it learns from training and observing the data and figures out the solution to the problem all by itself without human intervention. The number of highly expressive neurons in a Neural network makes it more feasible and easier for humans to interpret decisions and solve problems through technology. Hasani said, “Just changing the representation of neurons can enable you to explore new categories and solve extremely sophisticated problems that otherwise would’ve not been possible.” Moreover, experts say that we’ll have a more sophisticated and complex network inspired by nature in the future. The current model of the network is just the beginning of technological advancements. One of the most remarkable feats of the “Liquid” Neural network is its ability to work with insufficient knowledge. In a world where you have to program/feed every little piece of information into the machine to ensure maximum efficiency and performance, neural networks surpass every other technology. Its ability to work with insufficient knowledge, parallel processing, distributed memory, and fault detection and fixing. This seamless integration of information through the “Liquid” Neural network is making machines more robust and smarter. With improved resilience to unwanted or noisy data – “Liquid” neural networks are making machines more accurate.

An Introduction to “Liquid” Neural Networks

Image Source: Verizonconnect.com Nearly all car crashes are due to human error According to National Highway Traffic Safety Administration (NHTSA) data, 37,461 lives were lost on U.S. roads in 2016. And 94 percent of vehicle crashes are due to “human error.” NHTSA estimates that vehicle safety developments helped raise the annual number of lives saved from 115 in 1960 to 27,621 in 2012. Cumulatively, these improved safety technologies saved over 600,000 lives between 1960 and 2012. As we are moving toward full automation, there has been a lot of development in driver assistance. This is where a vehicle is controlled by the driver, but there are some driving assistance options like collision warning, automatic emergency braking, lane departure warning, and adaptive cruise control are already helping to save lives and prevent injuries. With self-driving technology and 5G, we could reduce car crashes and fatalities further. To understand how self-driving technology can make cars safer, we need to understand how fast humans can react to road hazards first. How Fast Humans Can React to Road Hazards A new study by MIT researchers shows humans need about 390 to 600 ms to detect and react to road hazards, given only a single glance at the road. The results indicate that younger drivers are quicker at both tasks: Older drivers (55 to 69 years old) required 403 ms to detect hazards in videos, and 605 ms to choose how they would avoid the hazard. Younger drivers (20 to 25 years old) only needed 220 ms to detect and 388 ms to choose from. When a child runs onto the road, it takes an average of 1.6 seconds for a human driver to hit the brakes. Self-driving cars equipped with radar or lidar sensors and a camera system have a reaction time of 0.5 seconds. A self-driving car can react much faster than humans. Reaction time is also a critical factor when drivers are tired. Self-driving cars ensure drivers will have enough time to safely take the controls and steer clear of unexpected hazards. Let’s look at how self-driving cars can offer better safety protection. The Science of Self Driving Cars According to Wikipedia, a self-driving car, also known as an autonomous vehicle, driverless car, or robo-car is a vehicle that is capable of sensing its environment and moving safely with little or no human input. They combine sensors and software to control, navigate, and drive the vehicle. Figure 1 below is an example of what self-driving cars look like. Source: Toward Data Science The camera, LiDAR, Radar, and sensors are like the eyes and ears of humans. And the computer and control electronics work like the brains hands, and feet of the humans. With the combination of hardware and software, many accidents can be avoided. Seeing is believing. The link to the video below shows how self-driving technology steps in to avoid a last-minute collision. This Video Shows a Tesla Model 3 Stopping Itself to Avoid an Accident As I was watching the video, the question came to my mind was “can I react as quickly as the self-driving car?” This is how machines can help humans to improve response time and compensate for the limitation of human vision. Why 5G is Critical For Road Safety What does 5G have to do with self-driving cars and safety? 5G’s increased throughput, reliability, availability, and lower latency will enable new safety-sensitive applications which are known as V2X or Vehicle-to-Everything. The low latency is important for real-time decision-making scenarios. Self-driving cars can generate terabytes of data daily. See Figure 2. Anything over a hundred milliseconds of latency is going to disrupt the operation of self-driving cars. Figure 2. Amount of data generated by self-driving cars. Source: Intel Corporation 5G specifies one millisecond (ms) end-to-end transmission latency requirements which are perfect for minimizing the V2X communications reaction time. With the existing 4G LTE system, it has various limitations preventing 1ms end-to-end transmission such as the 1ms length of subframes. As a result, 4G LTE will exceed the 1ms end-to-end transmission requirement just to transmit the data. The bandwidth improvements with 5G will improve the data transfer rate. 5G’s high bandwidth data rate of up to 20 Gb/s enables applications like real-time mapping for automated driving, software updates, and streaming multimedia infotainment. Road Safety Improved When Cars Are Connected Figure 3. Communication of Car Crash via V2X. Source: ZDNet. When cars with V2X are connected via the 5G network, safety is further improved with services such as collision avoidance. See Figure 3. Vehicles involved in the accident can send messages to the cars behind them to warn the other drivers about the car crash ahead of them. It will take time for the 5G infrastructure to build up. When 5G is adopted widely it will offer much better safety protection for drivers and pedestrians. One Common Safety Framework For Self-Driving Cars For many years, federal car-safety regulation is based on the Federal Motor Vehicle Safety Standards (FMVSS). With significant safety improvements brought about by self-driving cars, the NHTSA is now looking at regulations relating to the development of vehicles equipped with Automated Driving Systems (ADS). While the large-scale deployment of self-driving technology is probably years away, many companies are actively developing and testing ADS technology throughout the United States. The automakers need a common framework to develop safety features that can prevent accidents. The creation of a governmental safety framework specifically tailored to ADS will help to speed up the development and deployment of self-driving cars. This in turn will make roads safer for everyone. References Automated Vehicles for Safety The study measures how fast humans react to road hazards NHTSA Frames Safety for Cars without Drivers NHTSA is Defining Safety for Self-Driving Cars, But It Has Questions For You Edge Computing Is So Fun - Part 2: The Future Begins with The Road Side Unit

Can You React Faster than a Self-Driving Car on 5G Networks?

10 years may seem like a long time, but it can feel like an entire lifetime in the world of technology. When predicting the biggest technology trends of 2010, the news service foresaw a year built on Blackberry’s emergence into the touch-screen mobile phone markets, users streaming their favourite series on their TVs, and the Microsoft Kinect sparking a gaming revolution. CNN ran an article While there’s a good chance that many younger readers will have no idea whatsoever about the market-leading , or the hotly anticipated arrival Kinect, it’s fair to say that these topics did spark excitement in consumers a decade ago. Blackberrys of yesteryear However, technology moves fast, and many of CNN’s mentions have since fallen into obscurity. Despite this, 2020 feels like a true watershed year for the world of technology, and plenty of the releases we can expect to see in the coming months will stay with us throughout the decade in a way that 2010 couldn’t quite match up to. For investors, the following perspective may present a great opportunity to that are still at an early development stage, including remote AR and 5G. And with apps like and , the process to investing is becoming even more simpler. invest in emerging businesses ARQ PCF So what is there to get excited for in the coming months? Here’s a shortlist of some of the biggest upcoming trends ahead for 2020: 5G accelerated change It seems mind-boggling to think that in 2010 we were managing to get by on just 3G connectivity. Today, even 4G data can feel unbearably slow at times. Luckily, 2020 is expected to be the year that the fifth generation of mobile data arrives for consumers. You may feel excited knowing that you’ll be able to browse your favourite news sites or social networks faster on your smartphones - - but the wider implications of 5G should be the most noteworthy facet of the new technology. multiple times faster in fact Using 5G, the connection speeds of internet-enabled gadgets and tech can improve exponentially. This aids the rise of the Internet of Things as well as cutting edge tech like self-driving cars and artificial intelligence. 5G can leverage truly immersive experiences within the realms of augmented reality and virtual reality, too - as shown in this still from an EE mobile phone network advert earlier in the year: The above image demonstrates how augmented reality in the era of 5G can pave the way for users to superimpose entire renderings of football matches onto a real-life backdrop. The rise of AI & AR One of the key beneficiaries of the arrival of 5G will be artificial intelligence. Driven by the power of faster data, we can expect to see AI leverage the power of convenience via enhanced automation. Hardware like is already being designed to act as a mind-reading wearable headset, while fast-food companies like Dominos are attempting to harness the potential of drones and robot delivery services for the not-too-distant future. AlterEgo We can expect to see heavier AI-driven projects like self-driving cars and taxis to make some bold steps in 2020, too. On the other hand, is pretty much everywhere when it comes to entertainment and gaming - it doesn’t take too long before you pass a gamer on the street. Augmented Reality Pokemon Go In the meantime, AR is not only about entertainment and gaming. Companies like focus on crafting AR, VR & MR experiences for businesses (e.g. interactive content for publishers). Mesmerise Privacy measures While we’ve been used to seeing steady improvements within cybersecurity for some time now, 2020 will be a pivotal year for technology to begin to effectively combat rising security threats online. The continuing evolution of cybersecurity will carry on over the coming months in a bid to combat the increasingly aggressive measures at the disposal of hackers as they attempt to access their victims’ personal data. points to some significant statistics that indicate 2020 will be a significant year for security online. In fact, the number of cybersecurity jobs is currently growing three times faster than any other field of tech profession. Simplilearn 2020 will bring an added emphasis on filling the rising number of cybersecurity vacancies as the industry continues to grow, and provided there’s enough of a market of professionals to fulfil the demand of this industry, there will be plenty of more ambitious developments geared towards keeping us safe over the next twelve months. Trust in blockchain Image Source: Blockgeeks our collective consciousness alongside the rise of Bitcoin in 2017 and its practical applications have been hard to ignore ever since. Blockchain entered Acting as a form of distributed ledger, Blockchain is essentially a list of untamperable records that are chronologically ordered and cryptographically signed. We’ve already discussed the importance of cybersecurity in 2020, and Blockchain’s ability to keep sensitive records secure means that the technology can provide users with an unparalleled level of privacy online. Assets can seamlessly be traced back to their origin using Blockchain, which is a great tool for traditional assets, but foresees the technology having the ability to trace food-based illnesses back to the original supplier too. It can also act as an automatic middleman when multiple parties wish to strike a trade deal with each other without the need for a centralised authority. Gartner The key and cutting-edge appeals of blockchain comes from its shared and distributed ledger, encryption capabilities, tokenisation and its distributed public consensus mechanism - which makes its records extremely difficult to hack into. Today, there are fears that the powerful new technology is still too far into its infancy for practical usage on a broad scale, but 2020 looks set to be the year where we see Blockchain beginning to find practical applications away from the chaotic world of crypto-assets. Practicality in cryptocurrencies Speaking of crypto-assets, in attempting to launch Libra, their revolutionary new foray into the cryptocurrency markets. With approximately one-quarter of the currency’s having already jumped ship - there’s diminishing hope of the cryptocurrency delivering on the promise it mustered up earlier in 2019. Facebook has had a torrid time 29 founding members However, the significance of Facebook’s sights being set on releasing a cryptocurrency has caused the market to wake up to new innovations as we move into the next decade of finance. Libra is designed to act as a . When you hear the name ‘Bitcoin’ the chances are that you’ll envision rollercoaster share prices punctuated by astronomical values and basement price drops. This, however, isn’t the case for a stablecoin. This is because stablecoins like Libra will be tied to real-world assets, like the US Dollar and gold, as a means of ensuring that its price stays - well - stable. stablecoin The problem with the cryptocurrencies of yesteryear is that their volatile prices means that there is very little practical use for the Bitcoin and Ethereum holders of this world. That’s where Facebook planned to step in with Libra. The implications of Libra were exciting - and this can still be the case. Stablecoins can bring banking to the unbanked areas of Central Africa and Asia and can potentially pave the way to a universal currency to be used worldwide. Libra will be one of many stablecoin cryptocurrencies to enter the market in 2020, and this newfound wave of technological stability could ultimately herald a newfound era of alternative finance. Only time will tell.

An Early Glance Into The Best Tech Trends: 2020 Edition

Language models (LMs) represent a highly versatile category of technology, processing text inputs to generate textual outputs. Contemporary research has illuminated that this adaptable text-to-text framework can be harnessed to address a myriad of tasks without necessitating task-specific adjustments (i.e., absent fine-tuning or architectural changes) through the use of prompting methods to enable precise zero and few-shot inference. In essence, LMs are initially trained on extensive, unlabelled text corpora (guided by a language modeling objective), after which the LMs are directed via textual prompts to resolve specific problems, thereby allowing the pre-trained model to be efficiently repurposed for varied applications. Despite the immense promise of LMs as task-neutral foundation models, initial endeavours to apply pre-trained LMs to downstream tasks (e.g., GPT and GPT-2 [4, 5]) encountered significant challenges. This overview will explore how recent advancements have improved upon these preliminary efforts, leading to LMs that deliver significantly enhanced task-neutral performance. A critical discovery in this research trajectory is the enhanced capability of LMs as they are scaled up. (from [1] and [2]) More detailed examination reveals that large LMs (LLMs) are (i) more sample-efficient compared to their smaller counterparts and (ii) exhibit greater proficiency in task-neutral transfer to downstream applications. Notably, the performance of these LLMs demonstrates predictable patterns relative to various parameters (e.g., model scale and training data volume). This empirical evidence has culminated in the development of GPT-3, a colossal LLM with 175 billion parameters, which not only surpasses its predecessors in task-neutral performance but also exceeds contemporary, supervised deep learning approaches in certain domains. Key concepts necessary for understanding LMs have been discussed in my previous posts, including the language modeling objective, the architecture of decoder-only transformer models, and their integration to forge robust foundation models. For a more thorough understanding, refer to the provided link. Background I will offer a succinct overview of these concepts here, while also delineating a few additional notions pertinent to comprehending LLMs like GPT-3. Modern LMs utilize standard pre-training methods to tackle a broad spectrum of tasks without subsequent modifications (i.e., no architectural adjustments, fine-tuning, etc.). Employing a voluminous corpus of unlabelled text, we pre-train our LM with a language modeling objective that (i) extracts a text sample from our corpus and (ii) attempts to predict the succeeding word. This represents a form of self-supervised learning, where the correct next word can always be verified by examining the data in our corpus. Language modeling at a glance Modern LMs employ decoder-only transformer architectures, which apply a series of layers that include masked self-attention and forward transformations to the input. Masked self-attention, as opposed to bidirectional self-attention, precludes the model from 'peeking ahead' in a sequence to predict the subsequent word. Architecture Beyond these decoder-only layers, the LM architecture encompasses embedding layers that store vectors for all potential tokens within a predefined vocabulary size. Through these embedding layers, raw text is transformed into a matrix that the model can process, achieved by: Tokenizing raw text into discrete tokens (i.e., words or sub-words), Retrieving the corresponding embedding vector for each token, Concatenating these token embeddings to form a matrix or sequence of token vectors, Integrating position (and other) embeddings to each token vector. By pre-training LMs over expansive text corpora, we obtain models capable of accurately predicting the next token within a given token sequence. However, to utilize these models for language understanding tasks such as sentence classification and language translation, we do not modify the LM. Instead, we leverage the model’s inherent generic text-to-text input-output structure by supplying textual "prompts," such as: Adaptation “Translate this sentence to English: <sentence> =>”
“Summarize the following document: <document> =>”. With these problem-solving prompts, a proficient LM should yield a textual sequence that addresses the query effectively. For scenarios requiring a selection from a predetermined set of responses (e.g., multiple choice or classification), the LM can be used to assess the likelihood of generating each potential response and select the most probable outcome. The essence of modern LLMs lies in utilizing language model pre-training as a mechanism to develop generic foundation models capable of resolving diverse challenges without the need for adaptation or fine-tuning. While previous LMs like GPT and GPT-2 [4, 5] demonstrated inferior performance relative to tailored or supervised language understanding methods, this learning paradigm holds considerable promise and—as evidenced by GPT-3—can yield impressive results when the underlying LM is substantially enlarged. Main takeaway Power Laws In this overview, we will frequently refer to the concept of power laws. To illustrate, consider a statement from a research paper: "The LM’s test loss demonstrates a power law dependency on the number of model parameters." This assertion establishes a correlation between two variables—the loss and the number of model parameters—wherein adjustments to one variable induce proportionate, scale-independent alterations in the other. For clarity, a power law is mathematically expressed as follows. In this equation, x and y represent the variables under examination, while a and p influence the power law's shape and dynamics. When this power law (with a = 1, p = 0.5, and 0 < x, y < 1) is graphed, converting both axes to a logarithmic scale reveals a distinct linear trend, characteristic of power laws. Power laws describe how one variable scales as a power of another. Our discussions will include an inverse version of a power law, represented as: This equation employs a negative exponent for p, producing a graph where one variable decreases as the other increases. In our analysis of LMs, we observe that the LM loss decreases in accordance with a power law across various dimensions, such as model or dataset size. Further elaboration on this will follow in subsequent sections. Additional Useful Concepts In addition to the foundational principles of language modeling, there are several ancillary concepts that will be beneficial to understand moving forward. The primary focus of the papers discussed in this overview is on scaling models like GPT and GPT-2 [4, 5]. As models grow in complexity, training them becomes increasingly challenging due to heightened computational and memory demands. Distributed training methods, which utilize additional hardware resources (e.g., more servers/GPUs), facilitate the scalability of large training operations. Distributed Training There are various strategies for distributing the training workload of neural networks. One such strategy is data parallel training, which involves: Taking a large mini-batch, Dividing it into smaller sub-batches, Performing computations for each sub-batch in parallel across different GPUs, Consolidating the results from each GPU to update the central model. This method enhances training efficiency by parallelizing computations across multiple GPUs. Alternatively, model-parallel training divides the model itself across several GPUs. For instance, each layer—or even sub-components of each layer—might be allocated to different GPUs. This distribution allows the forward pass of the training process to occur across multiple devices, enabling the training of larger models and potentially improving efficiency through sophisticated pipelining and parallelization. For the purposes of this overview, it is important to recognize that leveraging multiple GPUs can make training of large language models (LLMs) more manageable and efficient. Data and model parallel training represent popular techniques in distributed training. There are numerous other strategies and considerations in this field that significantly enhance practical outcomes in deep learning. Utilizing large batches in data parallel training is generally advantageous for computational efficiency. However, excessively large batches may impair model performance and escalate computational costs, requiring more hardware. This phenomenon of diminishing returns becomes evident beyond a certain batch size; see below. Critical Batch Size (from [3]) Research has empirically determined the 'critical batch size', a metric that estimates the optimal batch size across various applications. Beyond this critical size, performance gains and computational efficiency start to diminish. As we will explore, some research adopts this critical batch size as a standard for efficient resource utilization during training. Language models generate text sequences in response to prompts through an autoregressive process, predicting each subsequent word based on the previous context. The simplest method continuously selects the most likely next word, but this approach may not yield the optimal overall sequence. Beam Search Beam search offers an alternative: instead of choosing the single most probable next word, it selects the top-k most probable options at each step, maintaining a list of potential sequences. At the conclusion of the generation process, the most likely overall sequence from this list is selected. This method provides a more effective approximation of the best sequence by considering multiple potential continuations at each step, thereby enhancing the quality of the generated text. Key Insights and Strategic Implications The foundational goal of GPT models was to develop universal language models capable of addressing a broad spectrum of tasks without necessitating task-specific tuning. This ambition is grounded in the premise that a profound comprehension of language modeling—specifically, the prediction of subsequent words in sequences—can be extrapolated to a variety of applications. Initially, models such as GPT and GPT-2 did not meet these expectations, delivering subpar performance in task-agnostic settings compared to supervised models. However, this overview has demonstrated that amplifying the scale of these language models (LMs) is an effective strategy for achieving high-performance, task-agnostic language understanding capabilities. This approach culminated in the conceptualization and deployment of GPT-3, an enormously scaled language model (approximately 100 times larger than GPT-2) that significantly outstrips the task-agnostic performance of its predecessors. The principle of scaling—enhancing model performance through the augmentation of model size, data volume, and computational resources—has been pivotal. Empirical data suggest that to optimize the efficacy of language model training, it is critical to substantially expand the model's dimensions while moderately increasing the dataset and batch size used during pre-training. This results in models that not only exhibit greater sample efficiency but also improve performance in accordance with power laws related to model size, data volume, and computational investment. Scaling Laws The advent of GPT-3, a model boasting 175 billion parameters, exemplifies the practical validation of these scaling theories. When such a colossal model is pre-trained across extensive text corpora, it demonstrates marked enhancements in task-agnostic, few-shot learning capabilities. Although GPT-3 may still lag behind specialized, supervised methods in certain benchmarks, subsequent studies have convincingly shown that as LLMs increase in size, their capacity for in-context learning also intensifies. Despite GPT-3’s architectural similarities to GPT-2, engineering a model of this magnitude underscores the transformative potential and scalability of foundational language models. Prospects of Scaling In conclusion, the journey from the initial GPT models to GPT-3 highlights a significant shift in the understanding and application of language models. The scalability of these systems not only advances their performance but also broadens their applicability across a diverse range of language understanding tasks, setting a new benchmark for future developments in the field of artificial intelligence. Bibliography [1] Kaplan, Jared, et al. “Scaling laws for neural language models.” arXiv preprint arXiv:2001.08361 (2020). [2] Brown, Tom, et al. “Language models are few-shot learners.” Advances in neural information processing systems 33 (2020): 1877–1901. [3] McCandlish, Sam, et al. “An empirical model of large-batch training.” arXiv preprint arXiv:1812.06162 (2018). [4] Radford, Alec, et al. “Improving language understanding by generative pre-training.” (2018). [5] Radford, Alec, et al. “Language Models are Unsupervised Multitask Learners.” [6] Child, Rewon, et al. “Generating long sequences with sparse transformers.” arXiv preprint arXiv:1904.10509 (2019). [7] Raffel, Colin, et al. “Exploring the limits of transfer learning with a unified text-to-text transformer.” J. Mach. Learn. Res. 21.140 (2020): 1–67. [8] Liu, Yinhan, et al. “Roberta: A robustly optimized bert pretraining approach.” arXiv preprint arXiv:1907.11692 (2019).

Understanding Scaling Law through Data Science Lenses

Last week, two remarkable events occurred: Meta announced its Llama 3, the most capable and highly anticipated open large language model (LLM) to date, and Boston Dynamics introduced its new fully electric Atlas robot platform — a stark departure from their hydraulic robots! At first glance, these developments may seem unrelated, but they are actually deeply interconnected, with the potential to synergistically drive each other forward and reshape how we work with and implement AI. At the heart of this connection is the transformative power of advanced AI. Breakthroughs in natural language processing and machine learning, exemplified by Llama 3, extend beyond language alone. These techniques, from deep neural networks to reinforcement learning, are also propelling significant advancements in computer vision, motion planning, and robot control. As language models like Llama 3 continue to expand the boundaries of AI’s ability to understand and interact with the world, they also lay the groundwork for more intelligent and capable robots. The implications, of course, extend beyond communication. The same techniques used to train LLMs on vast amounts of text data are also applicable to learning from massive datasets of sensor readings, images, and simulations for robots. Moreover, the open-source nature of models like Llama 3 democratizes access to state-of-the-art AI, enabling a broader spectrum of researchers and companies to integrate these capabilities into their robotic systems. At least start playing on that field! Every successful (and unsuccessful) attempt by Atlas to manipulate an object, navigate a cluttered factory floor, or assist a human worker becomes a valuable data point for the model, just as it does for Llama 3. Using this innovative model, Meta developed a standalone app, meta.ai, and embedded it into WhatsApp, Instagram, and Facebook, collecting vast and highly personalized data. This could lead us toward synthetic social networks or — which is more likely — an extremely personalized, embodied AI experience. As Llama 3 and the electric Atlas converge, they may accelerate each other’s development, blurring the lines between software and hardware, bits and atoms. As language models become more proficient at understanding and interacting with the world, robots will become more capable of applying that knowledge. This doesn’t necessarily mean evil. More capable and advanced robots, which can be fine-tuned and improved with your personal data and specifics, could take over mundane tasks at home. Imagine, instead of hundreds of text editors, finally having a robot that can load and unload the dishwasher and do the laundry. Currently, launches like Llama 3 are seen as enhancing AI’s understanding and processing capabilities, but in the long term, they will be one of the milestones in building and deploying machines that are finely attuned and aligned with us to assist in our daily lives. https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true Other Impressive Models: (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper
Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper
Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper
Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper
Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper
Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper
YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper
RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper
RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper
JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper News from The Usual Suspects © Google open-sourced Gemini Cookbook
and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “capacity to deliver capable AI”
DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life Hugging Face https://x.com/QGallouedec/status/1782430246957994422?embedable=true Stanford published the 2024 AI Index report, reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. Enjoyed This Story? I write a weekly analysis of the AI world in the Turing Post newsletter. We aim to equip you with comprehensive knowledge and historical insights so you can make informed decisions about AI and ML. Last week, two remarkable events occurred: Meta announced its Llama 3 , the most capable and highly anticipated open large language model (LLM) to date, and Boston Dynamics introduced its new fully electric Atlas robot platform — a stark departure from their hydraulic robots! At first glance, these developments may seem unrelated, but they are actually deeply interconnected, with the potential to synergistically drive each other forward and reshape how we work with and implement AI. Llama 3 Llama 3 electric Atlas robot electric Atlas robot At the heart of this connection is the transformative power of advanced AI. Breakthroughs in natural language processing and machine learning, exemplified by Llama 3, extend beyond language alone. These techniques, from deep neural networks to reinforcement learning, are also propelling significant advancements in computer vision, motion planning, and robot control. As language models like Llama 3 continue to expand the boundaries of AI’s ability to understand and interact with the world, they also lay the groundwork for more intelligent and capable robots. The implications, of course, extend beyond communication. The same techniques used to train LLMs on vast amounts of text data are also applicable to learning from massive datasets of sensor readings, images, and simulations for robots. Moreover, the open-source nature of models like Llama 3 democratizes access to state-of-the-art AI, enabling a broader spectrum of researchers and companies to integrate these capabilities into their robotic systems. At least start playing on that field! Every successful (and unsuccessful) attempt by Atlas to manipulate an object, navigate a cluttered factory floor, or assist a human worker becomes a valuable data point for the model, just as it does for Llama 3. Using this innovative model, Meta developed a standalone app, meta.ai , and embedded it into WhatsApp, Instagram, and Facebook, collecting vast and highly personalized data. This could lead us toward synthetic social networks or — which is more likely — an extremely personalized, embodied AI experience. As Llama 3 and the electric Atlas converge, they may accelerate each other’s development, blurring the lines between software and hardware, bits and atoms. As language models become more proficient at understanding and interacting with the world, robots will become more capable of applying that knowledge. meta.ai meta.ai synthetic social networks synthetic social networks This doesn’t necessarily mean evil. More capable and advanced robots, which can be fine-tuned and improved with your personal data and specifics, could take over mundane tasks at home. Imagine, instead of hundreds of text editors, finally having a robot that can load and unload the dishwasher and do the laundry. Currently, launches like Llama 3 are seen as enhancing AI’s understanding and processing capabilities, but in the long term, they will be one of the milestones in building and deploying machines that are finely attuned and aligned with us to assist in our daily lives. https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true Other Impressive Models: (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper Mixtral-8x22B: →read the paper Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper Rerank 3: →read the paper Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper Idefics2: →read the paper Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper Reka Core, Flash, and Edge: →read the paper Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper Ferret-UI: →ead the paper Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper Zamba: →read the paper YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper YaART: →read the paper RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper RHO-1: →read the paper RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper RecurrentGemma: →read the paper JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper JetMoE: →read the paper News from The Usual Suspects © Google Google open-sourced Gemini Cookbook and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “capacity to deliver capable AI” DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life open-sourced Gemini Cookbook Gemini Cookbook and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “ capacity to deliver capable AI ” merging capacity to deliver capable AI DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life published Hugging Face Hugging Face https://x.com/QGallouedec/status/1782430246957994422?embedable=true https://x.com/QGallouedec/status/1782430246957994422?embedable=true Stanford Stanford published the 2024 AI Index report, reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. published the 2024 AI Index report , reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. the 2024 AI Index report Enjoyed This Story? I write a weekly analysis of the AI world in the Turing Post newsletter. We aim to equip you with comprehensive knowledge and historical insights so you can make informed decisions about AI and ML. Turing Post

AI Industries Converge: Llama 3 and Electric Atlas Have More In Common Than You Think

Product Hypothesis Validation: Best Practices & Examples

Humans! One interesting twinkle in the dark, vacuous expanse of the cosmos. Trapped on a floating ball of rock to mope into space and be tormented by relentless, conflicting emotions. Prison within and prison without. Awaiting the summon of death. What the hell is this existence and what is its use? At least we have each other. Right? Yes, we do. Plus, the resources of the earth. And most importantly, there's that small voice that keeps telling us to become more. So, we stagger on. Nothing is certain in this great maze of existence but we don't just give in. We grope for purpose in the confusing unknown. We do what we do in search of fulfillment. Thus, we solve problems for those we love. We really dig in and harvest ingenious creations from within us. It makes us happy, so we keep going. We keep chasing that which our heart yearns for. What is it many have asked? What is it that we really want? Some of the smartest among us have given a name to it, and I dare agree with them. It is what it is; Freedom! Freedom from wants, death, and ourselves. Freedom from whatever dares limit us. Freedom from this great bondage that is us. Hence, in our relentless yearning, we build this great bridge of science. Into matter, into space, into life. Into technology! Oh that we may find that which we seek. Oh that we may find; Autonomy! Autonomy Has Always Driven Technology Humans have always been bound by limitations that we constantly rebel against. Imagine the caveman days. Small, slow, and weak as we were, we were completely bound by the harsh laws of the jungle. Dominated by powerful creatures. Yet, our big brain brought us to the very top of the food chain. Inventions like the spear, knife, and arrow with advanced hunting strategies helped us overcome the powerful beasts that ruled the wild. Then, we created our own societies and were ruled by tribal laws. Not very different jungle laws though but strong humans were in charge now. Then came the reign of religion, and priests were in charge. Then came countries with cunning politicians. Then came capitalism with wealthy businessmen who are now running the show. All these changes have been driven by advancements as humans try to take more control of their lives. From being ruled by wild beasts to strong men to priests to politicians and businessmen, people have always invented systems that gave them more control of their lives. And this progress is not only evident in the technology of our leadership but also in the everyday devices that we use. First, we had caves and stone tools, then came villages and metal tools, and then came towns and analog machines. Now, we have cities and digital technology. This drive for autonomy in our technology has culminated in what we call a decentralized internet or web3. Autonomy in Web3 Web 3 remains a relatively recent technology that is yet to be widely understood. Yet, it is already being widely adopted as the advantages are very obvious. Simply put, web3 aims to put the Internet and financial system under the control of everyone and not just a few giant corporations. Why is this necessary you may ask? Well, the answer is simple: "Power corrupts and absolute power corrupts absolutely!" The internet and financial systems represent the most critical technologies that humans have been able to develop. Hence, there is a great power in the hands of anyone who controls them. It's no wonder that top government officials and tech billionaires dominate the list of most powerful people in the world. This great power which allows such people to exercise control around the world is akin to the legendary absolute power that absolutely corrupts the wielders. And we have already seen this corruption at play. For example; , , , etc. Facebook has recorded multiple user data breaches Twitter has been accused many times of stifling free speech US politicians were caught cheating through insider trading in the stock market Hence, the internet and financial market which have lasting effects on people’s lives need some level of democracy. This is what web3 promises. Cryptocurrencies were introduced as a means for people to transact directly without passing through middlemen in the banking institutions who often benefit fraudulently from people’s money with little or no punishment. Combining crypto with blockchain technology provides the world with a global decentralized financial internet that no one fully controls and everyone can benefit from. Is Autonomy Truly Possible? Many reservations have been raised about the practicality of web3 and whether it can be truly autonomous. For example; fundamental blockchain infrastructures are often hosted on web2 servers with centralized control. Also, mining cryptocurrency is energy intensive which makes the entire system dependent on power companies. Would the power companies and server owners not be the new bosses in town? Well, that's a critical problem that is yet to be solved. However, blockchain technology is slowly solving the centralized server problem through compressed distributed storage of the technology on the devices of its users. Basically, a blockchain is a database, and every device that connects to the blockchain has a version of the database stored on it. This makes it possible for the billions of devices that users use to connect to the blockchain to function as servers distributed around the world instead of just one central server. Fingers crossed for what will come in the future. About the electricity problem, well, most cryptocurrencies have a limited amount of tokens to be mined, hence, mining is not a forever thing. Also, alternative versions of power like solar make it possible for miners to set up affordable sources of personalized power. Hence, centralized power companies can possibly be bypassed. With these considerations, the autonomy that web3 promises can foreseeably be accomplished. Future of Autonomy Picture this... You fall asleep now and awaken in the autonomous age. Robots run the world now. The planet is one big country run by the United Nations and headquartered in Earth City (a giant circular artificial island built in the Atlantic Ocean on longitude 0 degrees and latitude 0 degrees). Networks of underwater hyperloop lines and speed train bridges interconnect all the continents in minutes and the former countries are nothing but political provinces for proper documentation and security. Autonomous vehicles with little or no accidents dominate the roads and autonomous spaceships transverse space to transport people and resources around the solar system. Humans have finally colonized the solar system. The world lives in abundance as automated high-tech farms meet all our agricultural needs. Basic amenities like water, electricity, and education are free, and everyone gets a living wage to cover secondary needs like real estate around the planets, internet access, and transportation. Advanced amenities like technology, art, entertainment, exploration, and financial trading are where businesses operate. Web3 is fully operational. Hologram devices are all over the place for people to experience the dinterverse (decentralized internet metaverse) which uses all those devices as servers. DAOs, running on open-source technology, are the global means of voting, with Auth ID being the prominent open-source platform for decentralized technology. Industry-based crypto coins are the means of exchange (e.g., techon for technology, agrocoin for agriculture, muse for music, etc.) and cryptex trading (forex trading of the time) is the primary means of employment for everyone with people speculating on which industry will do better based on the season. Bitcoin is, however, immortalized as the only currency accepted around the world across all industries. Global poverty has been eradicated and financial inequality is very low around the world with cryptex trading available to everyone. A decentralized financial system was the game changer as it led to an explosion of prosperity through relatively easy cryptex trading. You investigate your trade on your holophone. You're doing very well and will be making another $50,000 this week. You smile to yourself as you glance out the window of your noiseless starjet (silent speed jets that ferry people around the world in minutes). The city below is a great wonder as people said. You're on vacation in Earth City. It's your first time. You came for it. The statue of autonomy or "the human spirit" as it is fondly called. Many weep upon first beholding its glory. The symbol of human victory on the earth. It appears on the horizon. The shiny bust of a crowned androgynous human child stops at the waist so it's neither male nor female. Completely transparent as if entirely made of glass, hence, the name human spirit. With both hands upraised in freedom, it holds Bitcoin (the first global crypto) in the right hand and a scroll in the left. Your ship does the ceremonial circling of the statue. Then it passes the scroll and you behold those legendary words. You read it in a low voice. "We humans hold that all of us are born free. Free to live, free to love, free to prosper!" A smile crawls up your face, and you resolve to uphold this great motto. Then you hear a phone ring. A familiar ancient music. It gets louder and louder. It's your phone. You were dreaming of course. You curse as you pick up your phone. It's your boss calling you to return to work. You cut the call and sigh in disappointment. That dream was just beautiful. Then it occurs to you: The current world is one big nightmare! WHAT DOES THIS MEAN Web3 is upon us. Cryptocurrencies are everywhere and blockchain infrastructure gets more sophisticated every day. Many crypto millionaires roam the earth to prove the real value of the technology. Decentralization is on its way. Below are the predictions you should know about web3; Decentralization is the future. Digital commodities e.g., NFTs, in-game items, virtual real estate, and decentralized exchange (DEX) will create a new industry. Currencies will become more globalized. Companies will become smaller and more personalized. Self-service will become the new way of operating the financial industry. HOW DO I PREPARE FOR AUTONOMY Educate yourself on cryptocurrencies, blockchain technology, and the metaverse. Invest in promising crypto coins. Participate in public web3 forums. Fund web 3 projects if you can. Learn how to make money on your own from the internet.

Behold: The Autonomy We Always Sought

দেখুন: যে স্বায়ত্তশাসন আমরা সবসময় চেয়েছি

Siehe: Die Autonomie, die wir immer gesucht haben

He aquí: la autonomía que siempre buscamos

Voici : l’autonomie que nous avons toujours recherchée

देखिए: वह स्वायत्तता जो हम हमेशा से चाहते थे

見よ、私たちが常に求めていた自律性

보라: 우리가 항상 추구했던 자율성

Eis: a autonomia que sempre buscamos

Вот: автономия, к которой мы всегда стремились

Bakın: Her Zaman Aradığımız Özerklik

Kìa: Quyền tự chủ mà chúng tôi luôn tìm kiếm

看哪：我们一直寻求的自主权

Introduction Imagine standing on the edge of a vast ocean, the waves whispering secrets of the deep—secrets of power, potential, and transformation. This is not the ocean but the realm of and Machine Learning (ML), an expanse that's just as deep and mysterious. In this article, we embark on a voyage to demystify these technologies that are reshaping our world. Artificial Intelligence (AI) From the basic building blocks to the towering achievements of AI, this guide promises to illuminate the path for the overwhelmed, the curious, and the aspiring. Here, we not only uncover the essence of machine learning and its implications for the future but also empower you with the knowledge to navigate its waves and harness its power for innovation and growth. Welcome to your first step towards AI mastery. Topics that will be covered: The Foundations of AI and Machine Learning : Simplifying jargon for a clearer understanding. Demystifying Terminology : Tracing the journey from its inception to the current state. The Evolution of AI The Mechanics of Machine Learning : The fuel of AI. Understanding Data : How machines learn from data. Algorithms at Play : Diving into supervised, unsupervised, and reinforcement learning. The Spectrum of Learning Practical Applications and Real-World Impact : From smart assistants to personalized medicine. AI in Everyday Life : Success stories across industries—healthcare, finance, and more. Case Studies : Navigating the moral landscape. Ethics and AI The Foundations of AI and Machine Learning Demystifying Terminology: Simplifying Jargon for a Clearer Understanding The world of Artificial Intelligence (AI) is brimming with technical lingo that can seem daunting at first glance. At its core, AI is the science of making machines mimic human intelligence—learning, reasoning, and problem-solving. Machine Learning (ML), a subset of AI, focuses on the idea that machines can learn from data, identify patterns, and make decisions with minimal human intervention. By breaking down terms like 'neural networks' (inspired by the human brain to process information) and 'algorithm' (a set of rules for solving problems), we start demystifying this field, making it more approachable and understandable. The Evolution of AI: Tracing the Journey from Its Inception to the Current State The concept of AI dates back to the mid-20th century when the dream of creating intelligent machines first took shape. From the early days of simple algorithms to today’s advanced neural networks capable of deep learning, AI has undergone a remarkable evolution. This journey has been marked by milestones such as the development of the Turing Test, the chess match between IBM's Deep Blue and Garry Kasparov, and the advent of self-driving cars, showcasing the growing capabilities and potential of AI technologies. The Mechanics of Machine Learning Understanding Data: The Fuel of AI Data acts as the cornerstone of all AI and ML endeavors. It's the raw material that feeds algorithms, allowing them to learn and improve. Whether it's images, text, or numbers, the quality and quantity of data directly influence the performance of AI models. Data preprocessing, including cleaning and formatting, is a critical step to ensure that models learn from the best possible information. Algorithms at Play: How Machines Learn from Data Algorithms are the heart of machine learning, giving machines the rules and methods to process data and learn from it. These range from simple linear regression algorithms used for predicting numerical values to complex deep learning models that power voice recognition systems. The choice of algorithm depends on the type of data and the specific task at hand. The Spectrum of Learning: Diving into Supervised, Unsupervised, and Reinforcement Learning Machine learning is categorized into three primary types based on how models are taught to make decisions: : Models are trained using labeled data, learning to predict outcomes for new data. Supervised Learning : Models infer patterns from unlabeled data without specific instructions on what to learn. Unsupervised Learning : Models learn to make decisions by performing actions and receiving feedback in the form of rewards or penalties. Reinforcement Learning Practical Applications and Real-World Impact AI in Everyday Life: From Smart Assistants to Personalized Medicine AI has seamlessly integrated into daily life, powering smart assistants like Siri and Alexa, enabling personalized recommendations on streaming services, and advancing precision medicine by tailoring treatments to individual genetic profiles. These applications are just the tip of the iceberg, showcasing AI's potential to enhance and simplify human life. Case Studies: Success Stories Across Industries—Healthcare, Finance, and More In healthcare, AI is revolutionizing diagnostics and patient care through tools that can predict disease progression and personalize treatment plans. In finance, AI algorithms detect fraudulent transactions and automate trading strategies. Each case study highlights AI's transformative power across sectors, driving efficiency, innovation, and improved outcomes. Ethics and AI: Navigating the Moral Landscape As AI becomes more ingrained in society, ethical considerations come to the forefront. Issues such as privacy, security, and the potential for bias in AI models raise important questions about the responsible development and deployment of AI technologies. Navigating this moral landscape requires ongoing dialogue among technologists, ethicists, policymakers, and the public to ensure AI benefits all of humanity while minimizing harm. This exploration into the foundations, mechanics, and practical applications of AI and ML illustrates the profound impact these technologies have on our world. By understanding the principles that underpin AI, we can appreciate its potential and address the challenges it poses, guiding its development toward positive and ethical outcomes. *—the grand tour through the labyrinth of AI and Machine Learning, a journey I daresay has been more enlightening than bewildering. We've untangled the spaghetti of jargon, traipsed down the memory lane of AI's illustrious past, and peeked under the hood at the cogs and wheels that make machines almost as smart as a well-trained Labrador—almost. Ah, there we have it We've meandered through the garden of AI's real-world blossoms, from chatty assistants who still can't quite grasp sarcasm to the wizardry behind popping just the right ad at just the right time (creepy, yet impressive). And who could forget our foray into the ethical bramble patch? A place where philosophers and programmers meet for tea and tussles over the moral fiber of our silicon companions. Now, as we stand at the precipice of the future, pondering, "What does tomorrow hold for work in an AI-infused wonderland?" one might feel a tidbit of good old fashioned angst. Fear not. While the crystal ball remains stubbornly cloudy, it's a safe bet that AI will not only take over the tedious tasks but also become the muse for our creativity, pushing us to invent new ways to procrastinate effectively. So, dear reader, as you ponder whether robots will steal your job or simply make it easier to pretend you're working while binge-watching your favorite series, remember this: the future of AI is as much in our hands as it is in the circuits of the next breakthrough algorithm. The question then is not if AI will reshape our world but how we choose to dance with it. Will we lead with grace, ensuring that as we sprint toward tomorrow, we don't trample over the values we hold dear? In wrapping up this delightful saunter through the realms of AI, let's not bid adieu but rather a cheeky "see you later." For the story of AI and human collaboration is only just beginning. It's a narrative brimming with potential plot twists, unexpected heroes, and perhaps a blooper reel worth of missteps. As we march arm-in-arm into the future, let's do so with a twinkle in our eye and a steadfast resolve to make it a journey worth tweeting about. And remember, as we forge ahead into this brave new world of algorithms and automation, keep your humor close, your ethics closer, and your curiosity unbounded. After all, in the grand tapestry of the universe, we're simply trying to teach old silicon new tricks. How hard can it be? Ta-ta! Jin Park Computer Science Student and Redbull-fueled nerd. If my digital doodles tickle your fancy and you fancy fueling my caffeine crusade, consider tipping the jar at . Every sip powers another quip! https://ko-fi.com/aloneinseoul

GLANCE NETWORKS

ww2.glance.net

-- employees

Since n.d.

COMPANY RANKING

GLANCE NETWORKS

EVERGREEN INDEX #4920

Representing this company? Click here to claim and customize this page!

Glance Networks WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

GLANCE NETWORKS

ww2.glance.net

-- employees

Since n.d.

COMPANY RANKING

GLANCE NETWORKS

EVERGREEN INDEX #4920

RELATED COMPANIESRANK

STORIES ABOUT GLANCE NETWORKS

Representing this company? Click here to claim and customize this page!

Glance Networks WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!