Through the development of a first of its kind open platform, Platform Science provides the hardware and software that allows clients to fully capture, analyze and apply a wealth of data, dramatically increasing performance, compliance, time management and savings

Platform Science

HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “Startups of the Year” award, featuring the most promising and innovative companies in the San Diego startup ecosystem. San Diego, CA - May 24, 2023 - https://www.youtube.com/watch?v=ZqIW54yMsNc&embedable=true The list, compiled by the HackerNoon team, showcases that are making significant contributions to the San Diego economy and driving innovation in their respective industries. 133 startups The nominated for Startups of the Year 2023 include: Startups in San Diego GoFormz Lively Root Platform Science Kahoona Tag-N-Trac Launch Factory comma Zoox Streamlined Robin Healthcare KÉXY ChargeNet Stations Replay RobustPhysics Sensorydata Skillmasters.io Truepic Blueboard 6Storage XRA Lawmatics Brain Doctible Classy.org Helix GoShare Sportidia The Good Face Project Cloudbeds ClickUp You can nominate your company as the . startup of the year in San Diego here “We are thrilled to recognize the top startups in San Diego and celebrate their achievements,” said . “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” David Smooke, HackerNoon Founder & CEO is a community-driven voting platform that recognizes startups that are changing technology and the world for the better. Starting May 8th, 2023, the internet can nominate and vote for the best ventures in their city. The voting period will remain open until December 31, 2023, and winners will be announced in January 2024 after a comprehensive evaluation by the HackerNoon team. Startups of the Year 2023 In its , a total of 44,427 startups were nominated in 16 different regions, and the community cast 234,344 votes during a 6-month long campaign in favor of budding companies that were growing and trending across every major city in the world. The received perks from sponsors and a free from HackerNoon. previous iteration 1,382 winners tech company news page As a proponent of the open data movement, HackerNoon releases the unique social data gathered from Startups of the Year on GitHub as “ ” for valuable insights. /where-startups-trend Apart from validation from the world's greenest tech publication, all nominees relevant to their domain, region, and funding. They will also receive an and a free version of their very own on HackerNoon. get free interviews amazing discount package designed for their startup Tech Company News Page These pages include business overviews (such as head count, founding year, bio, social links, and domain ranking) coupled with dynamic updates every time the company is mentioned in HackerNoon stories or relevant headlines around the web. People can , and vote once per day per award for who they think should be Startup of the Year all the way up to December 31st. nominate their favorite startups until June 8th In addition to the award itself, winners receive exclusive interviews, an exclusive NFT from HackerNoon, and a .Tech Domain for life. For more information about the “Startups of the Year 2023” list and the nominated companies, please visit our . FAQ page About HackerNoon HackerNoon is a technology publication that aims to empower developers, entrepreneurs, and tech enthusiasts to share their knowledge and expertise with a global audience. With millions of monthly readers, over 500,000 followers on social media, and over 250,000 newsletter subscribers, HackerNoon provides a platform for startups and innovators to showcase their work and connect with a vibrant community of like-minded individuals. Did you know that companies that actively engage in blogging experience a whopping 67% increase in leads? Take advantage of HackerNoon's ' ' to kickstart your own company's blogging journey. Startups Package Startups Of The Year 2023 is sponsored by . .Tech Domains You can nominate your company as the . startup of the year in San Diego here

Startups of The Year 2023: 130+ Startups Nominated in San Diego

Exploring the Top Data Science and Machine Learning (DSML) Platforms of 2022 

Hello, in the first article of this series, I will examine the KNIME data science software. I will also explain how to install the KNIME application, which is written in Java and built on Eclipse. This application can prepare data science and artificial intelligence applications with workflow-style visual programming, and it can be configured on widely used GNU/Linux systems.  I will review its usage with a machine learning application and save the application I prepared in a Github repo. KNIME KNIME Analytics is a data science environment written in Java. This software allows visual programming in the form of a workflow using various nodes and is an application that allows to development of a data mining application even without knowing advanced coding. It has a very diverse and rich plugin center and is used quite often in academia as well. It is an extensible data science platform where user-created scripts and codes can be used as well as visual programming. KNIME is a cross-platform software that supports installation on many operating systems. You can click the to download. In this article, I will review KNIME for GNU/Linux distributions only. link Configuration You can extract the archive of the KNIME application to the desired location. tar.gz ~$ tar xvf knime-analytic-?.?.?.tar.gz -C /opt/knime All libraries and plugins for KNIME are available in the directory. There is a binary file named and you can start the application using it. But it needs to work. Knime java JAVA 11 KNIME Analytics works with Java 11 and higher. For this reason, you can install either or packages for KNIME. Depending on the distribution you use, you can easily download and install these JDKs from the main repository. openjdk-11 openjdk-latest For example, for RHEL based ones; ~$ sudo dnf/yum install java-11-openjdk # or java-latest-openjdk Or Debian based ones; ~$ sudo apt install openjdk-11-jdk # or default-jre Another option I can suggest here is the liberica JDK versions published by Belsoft. These are open-source and freely distributed Java JDK versions. You can download and install JDK with many options such as standard and full package. You can find installation scripts or archives [on this page] ( ) for many operating systems. For KNIME Analytics, standard JDK-11-lts is sufficient. For Linux, we need to download the tar.gz package and extract it to a directory. Then the and variables are updated and the Java installation is completed. https://bell-sw.com/pages/downloads/#mn JAVA_HOME PATH ~$ mkdir -p ~/libertica-jdks
~$ tar xvf /path/to/downloaded-jdk.tar.gz -C ~/libertica-jdks/

~$ export JAVA_HOME=~/libertica-jdks/jdk-11.?.?
~$ export PATH=$PATH:JAVA_HOME/bin
## after this
~$ java -version JDK is available in this session. If you want to use it in this location all the time, you should add the lines exported for JAVA_HOME and PATH to your or file if you are using zsh. .bashrc .zshrc After the Java installation, it is sufficient to go to the directory where KNIME is installed and run the script. If you have more than one JDK installed on your system, you can specifically give the path to JDK-11 with . ./knime ./knime -v /path/to/jdk-11/bin If you want to create a for this; dektop entry [Desktop Entry]
Type=Application
Name=KNIME Analytic Platform
Description=Data Science Environment
Exec=/path/to/knime-folder/knime_4.?.?/knime
Icon=/rpath/to/knime-folder/knime_4.?.?/icon.svg
Categories=Development;Science;
terminal=false You can create a file named and save the entry to the relevant location. If you have a problem with the exec command. You can give a shell script that runs the ./knime script as a parameter in the command. If you are using Wayland as a display server, you may have problems with the application. For this reason, you can add the line to your configuration file or or whatever shell you are using, or inside your script file that starts KNIME. If you're already using x11, you won't have any problems. For example, the shell script that starts KNIME; ~/.local/share/application/knime.desktop exec export GDK_BACKEND=x11 bashrc zshrc #/bin/bash
export GDK_BACKEND=x11
./knime -v /path/to/java-jdk-11/bin may form. Extension Installations The KNIME application had many extensions. The high number of users and its extensibility are the most important factors that bring it to the top, among other data mining applications. You can open the interface by selecting from the menu in the application. You can install it by searching for the add-on you want. For example, you can install the plugin that should be used to connect to the Twitter API and pull data. It will be installed with the necessary dependency packages. The application will need to be restarted before it can read new plugins. Install KNIME Extensions... File KNIME twitter connector Then you can search for the node you want from the section and add it to your project. Node Repository Machine Learning Application with KNIME In this section, a ready-made dataset will be used to prepare a machine learning application. A data mining workflow will be created with KNME nodes by using the dataset named in the . The project is created with from the menu. HCV Data Dataset UCI Machine Learning Repository new project File Node Repository I add my file to the project by searching or from the section. Similarly, I search for the transactions I want to do and add them to my project and run them, and at the end of each operation, I give the output as the input of the next node. file reader csv reader Node Repository When I right-click on the nodes, I configure the operation and then run it. When I right-click again, I can see the output of the node as a result of the operation under the menu. The node below outputs the normalized table and model. Normalize The operations performed in this application can be seen as follows. The operations performed in this application can be seen as follows. In the application, the data in the CSV file was first read with the node and the categorical values ​​such as were given to the node to be converted into numbers. The output of this node is given to the node to extract the Id and categorical gender values. The output dataset is given to the node. The dataset with 0-1 normalization was given to the node to be divided into 70% training and 30% testing. A model was created by giving the training dataset from the parts to the node. This model and test dataset was tested by giving it to the node. And the output of the node has been transformed into a table with the comparison matrix and score values ​​related to the performance in the node. csv reader sex category to number column filter Normalize Partitioning RProp MLP Learner MultiLayerPerceptron Predicter Scorer When our perceptron model was tested with 1 hidden layer and 10 neurons, it was seen that our comparison matrix and performance values, respectively; It seems to be a very successful model, here the dataset was not raw data. The performance of the data preprocessing on the data set was 98% accurate. Preprocessing is an important factor for model performance. If you create a model with the raw data set, you can see that you cannot achieve high success. Due to the preprocessing of this dataset; Reviewed with R Sample dataset review with Smote Outlier cleaning with R boxplot Second-time dataset with SMOTE balancing You can find this project and its dataset in the repo in the link. Resources and Project Github repo HCV Data Dataset Originally published . here

How to Install the KNIME Analytics Data Science Software

Hey Hackers! I’m and I’m the Project Manager of . Kahlil Corazo, AccessibleGenomics.org First of all, a huge thank you to the HackerNoon community and staff for nominating me for a 2021 Noonies award! I’ve been nominated in the following categories; please do check out these award pages and vote: HackerNoon Contributor of the Year - science: https://noonies.tech/award/2021-hackernoon-contributor-of-the-year-science As someone in the , I believe that the most exciting technology of the present is because, . Learn more about my thoughts and opinions on and my journey in the tech industry via the interview below. developing world open-source synthetic biology like open-source software, it will open up so many good things for us outside the developed world open-source synthetic biology 1. What do you do, and why do you do it? (tell us your story) You may remember May 2020, just at the start of the lockdowns due to the pandemic. I remember it quite well because I never felt so powerless in my life. I’m an entrepreneur, so I had this self-belief that I was the master of my fate, that I could influence the future. Yet, at that time, I was simply a prisoner of circumstance. Before the pandemic, I was already nerding out on nanopore sequencing. I was so interested because it allowed resource-constrained labs to finally have access to genetic sequencing. In the past, sequencing machines used to cost more than a car and were as big as refrigerators. But the MinION sequencer from Oxford Nanopore Technologies was as small as an iPhone and cheaper than one. I found an escape from my prison through that nerdy interest. I was not going to sit back and do nothing in this pandemic. I was going to contribute to facing it. When I reached out to the other nanopore nerds I met on Twitter, they immediately got the situation. Since we were in this world of genomics, we knew that communities need to sequence SARS-CoV-2 and track its most transmissible and deadliest variants. Yet, in the developing world, only a few labs have genetic sequencers. Why not send MinIONs to these labs and give them trainings? So we built a global team of volunteer students and scientists. We got a microgrant from Just One Giant Lab (JOGL.io), an open-science platform. We partnered with a lab in rural Philippines, PGC Mindanao. We organized trainings for their molecular biologists and bioinformaticians. In 10 months, that lab made history: the first sequencing of SARS-CoV-2 enabled by volunteers, and the first on-site sequencing in the region of Mindanao, Philippines. . . of any organism Here’s the pre-print Here’s the project management lessons learned The adventure did not stop there. Through that project, we discovered a more fundamental problem in doing molecular biology in the developing world: access to reagents. 2. Tell us more about the things you create/write/manage/build! We thought that the biggest threat in Project Accessible Genomics was the difficulty of training local personnel in sample preparation, sequencing, and bioinformatics. However, it turned out that as long as you have experienced molecular biologists and bioinformaticians, the technology is at the stage that training can just be done online. What almost killed the project, however, was access to reagents, the special chemicals you need to prepare samples for sequencing. We need to buy all of these reagents from the US or Europe. These are already expensive for a lab in the developing world. Then you need to pay for their transportation through a cold chain. And during the project, we found out that you also need to pay gatekeepers, both legal rent-seekers and corrupt ones! You could skip all these if you can manufacture reagents locally. Now is the time because of recent progress in open-source wetware and software, and lower-cost hardware. That was the topic of . my Hackernoon article Since writing that piece, I and some of my teammates from Project Accessible Genomics have started molave.bio, which will manufacture basic lab reagents for the local market. We are building with the garage door open: biokahlil.substack.com 3. How did you end up on your current career path? Do you like it? In 2018, I read Naval’s now-iconic Twitter thread, How to get rich (without getting lucky). . A key principle in that playbook is to bypass competition by following your genuine interests. I decided to make it my entrepreneurship OS I already built a company at that time, which was paying the bills but not scaling. I felt I needed to do more and something different. So I simply followed whatever I was most interested in and solved problems right in front of me. The domain that captured my curiosity (e.g., measured by the kinds of journal articles I read) was biology in general. So I enrolled in a master’s program. Digging further into my interests led me to genomics. The pandemic disrupted my schooling, but I learned so much more in running Project Accessible Genomics than school. The problem right in front of me now can be solved with synthetic biology, so I’m going through a steep learning curve right now in that domain. I just need to know enough to work with my scientist colleagues. My role is to do the dirty work so that the scientists can do more science. I’m just so fascinated by this domain, and I love working with super smart people! 4. What tech are you most excited or passionate about right now and why? Synthetic biology is the application of engineering principles to biology. We now have a much better understanding of genomics and the translation of genes into proteins that form components of living bodies. We also have better instruments to make sense of this microscopic world and to make precise modifications. Because of these advancements, it is now possible to produce complex and valuable molecules through engineered bacteria and fungi. When insulin was first synthesized through engineered bacteria in the late 70s’ (which has saved millions of lives) this capability was limited to massive biotech companies in the developed world. Today, companies in Africa (which we are currently aiming to replicate) are using similar techniques to produce lab reagents locally. The most valuable products of synthetic biology will be medicines, hormones and other products for human health. The same technology can be used for agriculture and food security. Synthetic biology is producing enzymes for labs, manufacturing plants, and as components of consumer goods. Complex molecules that cannot be produced any other way are the tip of the spear. At some point, substances produced through synthetic biology could replace products from petrochemical (like some components of fragrances) or harvest from animals (e.g., collagen from cows). 5. What tech are you most worried about right now and why? Your spidey sense might have been tingling as you read the previous sections. With great power comes great responsibility. Some areas of synthetic biology have risks to the scientists working in those domains. And like any technology, synthetic biology could be used for criminal activity. 6. If we gave you 10 million dollars to invest in something today, what would you invest in and why? I’d invest in startups solving problems I’m curious about. It will be lots of fun. 7. What are you currently learning? I’m currently learning Spanish. I’m visiting Mexico for the first time next month. I’ve also been learning Olympic weightlifting from the first coach of the first Olympic gold medalist of my country, the Philippines. 8. What’s the best advice you’ve ever given someone? Great advice happens when you know the person and their situation, and you have first-hand experience in the area you are advising. I may have given some good advice in the past, but they are probably useless to the reader. And I have no idea which ones were actually valuable. I guess the spirit of this question is to share something useful to the reader. I prefer playbooks instead of advice in these cases. Here are some of my favorite playbooks: (known problem, known solution). I wrote a book: Project Management roamforpm.com (problem and solution not completely known). I’m using this for building molave.bio, and I’m doing it with the garage door open: Evidence-based Entrepreneurship biokahlil.substack.com (unknown problem, unknown solution, but you’re the right person to tackle it). Might be a future book. I have some Twitter threads and videos on this: Designing Serendipity designingserendipity.com 9. What’s the best advice you’ve ever received? I prefer playbooks over advice. There are two that stand out: Naval’s 2018 thread “How to get rich (without getting lucky)” for entrepreneurship Liz Gilbert’s book “Big Magic” for creative work About HackerNoon’s 2021 Noonie Awards The annual Noonie Awards celebrate the best and brightest of the tech industry, bringing together all who are making the Internet and the world of tech what it is today. Please be sure to , nominate, and vote for the people and companies who you think are making the biggest impact on the tech industry today. check out our award categories The 2021 Noonies are sponsored by: , , and . Thank you so much to these sponsors who are helping us celebrate the accomplishments of all our nominees. bybit Dottech Domains Avast

Hacking Synthetic Biology with Noonies Nominee Kahlil Corazo

Pecan.ai has just come out of stealth, , to enable business analysts to build machine learning models automatically. Dell Capital led the round, joined by S capital and bringing the total funding of the company to $15M. raising an $11M Series A The startup was founded in 2016 by Zohar Bronfman and Noam Brezis, two PhDs in computational neuroscience. The company now counts just under 30 employees and is opening offices in New York. Pecan.ai’s product is a platform targeted at business analysts, enabling them to automatically build predictive models. Their software connects to data sources, cleans up data, and automatically trains a model. A catalog of templates is available to get started, then advanced users can build their own. Right now, the catalog features templates such as fraud detection and churn use cases. This appears to be part of a second wave of companies bridging the technical gap between non-technical staff who know the business needs well, and machine learning capabilities. "It is about supply and demand. A lot of smaller companies are not in a position to hire data scientists because the good ones are expensive and rare. So we need to understand what other human capital is available", says Boaz Amidor, a strategic advisor to Pecan. The problem is not new, and some of the first wave of startups have already reached scale. Dataiku, the collaborative data science platform, was founded on somewhat similar claims of helping business intelligence staff collaborate with data scientists. Its drag and drop model building interface made it quite popular with less technical people. 7 year after inception, Dataiku is now following a secondary round by CapitalG, Google’s growth equity fund. Other platform companies in the space include DataRobot, who announced a in September, bringing its total funding to date $431 million. worth $1.4B $206 million series E Pecan will have to differentiate vs those more established players. I had a call with CEO Zohar Bronfman. When asked about differentiation, he pointed out to a higher level of automation of the data preparation stage: “we automate the entire data prep and engineering required for the autoML”. Indeed, data preparation and engineering is notoriously expensive and time consuming, . taking up to 80% of data scientist time That is true both for Pecan.ai’s business analyst target user, but also for machine learning engineers. Cleaning up data and selecting the right features efficiently is a topic that has attracted a lot of attention in the machine learning community. Some large, world-class machine learning teams noticed that individual contributors were all rebuilding the same, low-value add, data extraction pipelines for their own needs. Platforms targeted at technical users, such as , now incorporate Feature Stores to help save feature extraction pipelines for future reuse. Startups such as Logical Clocks are following suite, . Uber’s internal tool Michelangelo making this approach commercially available The key question here is to what extent extracting, cleaning and preprocessing data can be automated in general. Even automated predictive tools like Salesforce Einstein gave disappointing results to the users I talked to. How can a tool yield better results, across use cases, in an automated way? I dug deeper to understand Pecan's approach. specialized "What we do is we build [use-case specific] templates that guide business analysts through the data preparation stages. With a solution like Dataiku, you still need data scientists involved in some part of the process.  Pecan built this templating system so that business analysts can operate alone."  said Bronfman. As often, fancy technology is not the sole component: here Pecan's approach relies on mapping out data science processes, and building wizards that guide users through the data science process. Pecan.ai is entering a competitive space, with established players, but it is surfing two massive trends: bringing machine learning to less technical users, and automating the data preparation pipelines. It focuses on guiding business analysts through processes that previously required data scientists input. Doing so, it empowers companies without a dedicated data science team to build machine learning models. Pecan's template catalog approach might force it to expand slowly at first, focusing on use cases that are already covered. But this funding round gives it fuel to start aggressively expand sales.

Pecan.ai Raises 11 Million to Bring Machine Learning to Business Analysts

The MLOps Conference took place earlier this week at Hudson Mercantile in New York City. Experts from the New York Times, Twitter, Netflix and Iguazio, the host company, spoke about best practices and machine learning implementation throughout a variety of different organizations. I learned of the technological void that exists when data scientists want to implement machine learning. With this new context in mind, I can approach conversations with our data team from a new perspective, and take the time to understand how we can implement new models on our team. Machine learning as a technology has been around for more than 50 years, beginning with Arthur Samuel’s where his program helped the computer improve with each game of checkers it played in 1952. But despite this progress, the ability to deploy new models remains a challenge. In fact, the pipeline to deploy new models can take weeks or months with many models never making it to production. pioneering work at IBM This is the importance of applying DevOps methods to machine learning (MLOps). As explained by Julie Pitt and Ashish Rastogi of Netflix, data scientists are most involved with the solution of applicable technologies. The challenge is that companies need to have the entire infrastructure underlying those technologies in order to implement machine learning. Thankfully, new solutions and best practices are coming onto the market to address these problems.  Brittany Wills explained that Twitter created a Feature Store (essentially a library) where data scientists can publish their latest work. By sharing best practices and verified models across the organization, various teams no longer have to constantly build from scratch, enabling them to activate models in days rather than months. Additionally, David Aronchick from Microsoft argued for the importance of regularly training your model. The data that the model was originally trained with goes stale very quickly in production and if you’re not careful, this could lead to data drift. While a Feature Store may require organizational buy-in, focusing on the micro elements of one model can have monumental impacts on the way consumers experience your products. One of the most interesting technologies shared during the conference was Iguazio’s Nuclio. is creating the data science platform for production, enabling companies ranging from startups to large enterprises to introduce machine learning and AI into their products in a fast and scalable manner. Iguazio Orit Nissan-Messing, their VP of R&D, sat down with me to explain their latest innovation. Iguazio builds architecture to streamline data science to production. We recognize that data scientists waste a majority of their time on “plumbing” rather than building actual models. We seek to solve that problem. Could you explain what it is that Iguazio does? Nuclio is an open source serverless platform that seeks to automate the delivery of code and models to production. Nuclio is unique in that it addresses ML workloads and provide high-performance, it can increase data throughput or accelerate ML training while decreasing latency by scaling computation resources dynamically. Essentially, serverless technology enables companies to focus on the application without worrying about dev ops, performance, or scalability. And how does your latest innovation, Nuclio address this challenge? Our data science platform is unique to the market because we are the only one that can work on any cloud or even on-prem and handle any data type. We work with AWS and Microsoft to pull in data from their cloud resources, enabling companies to build insightful models with data that would have otherwise been siloed. Adding Nuclio supercharges this data and simplify the delivery of intelligent business application. How does this complement the work that Iguazio is already doing? The way in which you collect your data will ultimately have a large impact on the final model and application. It’s critical to ensure that your data is accessible and stored properly. When you have data from different sources you must also normalize that data to prevent false quantitative bias. Ultimately, the Product Managers are most responsible for driving the vision and bringing the business problems to the Data Scientists, but it’s helpful for them to have context around the ML infrastructure so that they can have a sense of cost to develop and reliability of the model. What what you tell Product Managers who rely on machine learning or are interested in learning more about it? Overall, it was an insightful glimpse into the infrastructure that underlies machine learning and how each component can still present an obstacle at a company who is keen to venture into ML. For Product Managers, here are a few key takeaways: – Product Managers are not data scientists and should instead focus on providing the problem that needs to be solved and why it’s valuable to commit resources to addressing it. For example, a company like Lyft or Uber may ask how many customers will adopt bike/scooter sharing in a new city? Bring the business problem – understand where the data comes from and how it could influence the application. Bias begins with the data, be sure to consider a variety of use cases. In this example, let’s assume that the company already has a city that has adopted bike/scooter sharing. Lyft and Uber both have bikes and scooters in San Francisco. With this in mind, consider how the usage data may be biased in SF. Some points of bias may be introduced by: the median age of the population in SF, the hilly terrain in the city may prevent usage in particular neighborhoods, population of SF has a large propensity to work in tech – does that influence their adoption of new technology? Or even, the number of single households vs. number of families. This is a similar exercise to considering any ambiguous opportunity that a PM must do and is highly critical when evaluating ML models. By understanding your data, you will be in a better position to understand the risk of this model and if and why it fails. Know your data – Ask your team how they approach building a model. Do they have to start from scratch? What blockers do they have? This will help you develop a better understanding of the cost/time to build so that you can make the appropriate trade off decisions. Learn how ML operates at your company In a large company, it is likely that the organization and/or individual teams have a process for implementing new ML models. However, as I learned from this conference, even Twitter only recently implemented their Feature Store to help various teams collaborate. It’s always worth asking how things work at your organization, don’t assume that everything is organized behind the curtain! Image credits Photo by from Aleksejs Bergmanis Pexels Human-centric Machine Learning Infrastructure @Netflix

The Rise of MLOps: What We Can All Learn from DevOps 

Building a Serverless Microservice CRUD RESTful API with MongoDB

In the last few years, Artificial Intelligence (AI) has gained momentum in every industry. Healthcare, transportation, manufacturing, logistics, retail, and more have started to use AI-based applications to improve productivity and performance. According to PwC, to the global economy by 2030. So businesses can reap huge benefits from investing in AI. The MIT Sloan Management Review’s found that 85% of executives believe that AI will help their businesses gain or sustain competitive advantage. Artificial Intelligence will contribute $15.7 trillion 2017 Artificial Intelligence Global Executive Study and Research Project The best way to evaluate any emerging technology is to figure out its practical use in your business. So here are some uses of AI applications that can lead you to a path of growth and success. 1. Use AI-based Analytics to Make Better Business Decisions The improvements in network and storage technology have brought about the age of big data. But the collected data is useless without analysis. Due to the large size, it’s impossible to analyze the data using human intelligence. It’s an area that machine learning and deep learning algorithms can help. , SAP’s in-memory data platform that uses machine learning to analyze and find patterns in big data, is . Walmart has more than 245 million customers visiting their stores and websites all over the world. So there is an enormous amount of data. HANA’s machine learning algorithms are bringing the important data to the forefront, so Walmart employees can make more data-driven decisions. HANA helping Walmart with their data analytics 2. Add Automation to Make Your Marketing and Sales Soar AI-based applications can start handling routine tasks. They can customize marketing and sales information for consumers. AI-based Chatbots can go further and help with responding to customer service requests to improve customer satisfaction and create recommendations for upselling. Machine learning can also help with price-optimization for various markets. , a data science platform, leverages data about competitors, suppliers, consumer preferences, and risks to create pricing models for individual market segments automatically. This AI-based approach helps businesses optimize profit margins. RapidMiner 3. Make Inventory and Supply Chain Management a Breeze with AI Machine learning algorithms can help retail and other businesses better manage their inventory. It can automate refilling requests and optimize supply chains. You can hand over your inventory management and supply chain decisions to AI-based applications. Major AI players like IBM Watson are heavily investing in supply chain management. help you automate your order management and fulfillment. Another company is combining machine learning and Internet of things (IoT) to create applications that predict supply chain movements. IBM’s supply chain products TransVoyant 4. Improve the Maintenance and Safety of Your Equipment Artificial Intelligence can help the manufacturing and transportation sectors improve their maintenance schedules. For example, the airline industry tries to predict the wear and tear on their fleet’s mechanical parts to prevent downtimes. AI-based predictive analysis can improve this process dramatically. It can help the airline industry create more optimized maintenance schedules to avoid unwanted delays. The manufacturing industry has been already using AI for maintenance and safety. General Electric (GE) has developed the that uses Artificial Intelligence to optimize and scale industrial applications. Predix platform 5. Get Better at Hiring and Find the Top Candidates Finding the right candidates for a job is a time-consuming process. There are new AI-based facial recognition applications that can interview candidates and evaluate their performance using emotional cues. It can help businesses streamline their recruitment process. Companies like Unilever, IBM, Dunkin Donuts are already using Artificial Intelligence to screen entry-level employees. Unilever has declared their AI-aided hiring a huge success. Applicants can give an interview using the on their smartphone. The app takes in the video and audio data from the interview, analyzes it and provides recommendations for the next steps to human recruiters. HireVue app 6. Use AI to Fight Fraud and Prevent Cybercrimes Businesses spend enormous time trying to detect fraudulent transactions. But fraud detection depends a lot on pattern recognition. So machine learning tools can pick these tasks up. Similarly, cyber security threats are also a matter of detecting pattern anomalies. So AI-based applications can help in this area too. The company is using machine learning techniques to develop solutions that can help companies better detect fraud. With rule-based systems, there are a lot of false positives. Machine learning algorithms can decrease these false alarms. deepsense.ai 7. Expand Your Business With Self-Driving Technologies Advances in self-driving cars are dependent on Artificial Intelligence. Businesses that depend heavily on transporting products can use self-driving trucks to lower costs and improve reliability. Manufacturing and other sectors can use autonomous cars to improve their operational efficiency. has announced in the 2018 Google I/O conference that it will start self-driving taxis in Arizona by the end of 2018. is preparing to release self-driving cars next year. has tested a fully unmanned truck in Florida and will start hauling commercial freight with unmanned trucks this year. These vehicles will open up new opportunities for businesses to improve current processes and create new business revenues. Waymo GM Starsky Robotics For business leaders who are looking to figure out Artificial Intelligence, the bad news is there is no shortcut. During the starting of the Internet, companies had to experiment to understand what worked for them. Businesses have to take a similar path of experimentation for Artificial Intelligence. Here are practical steps you can take to figure out your path. AI Development — Heaven or Hell? Depends on You. Evaluate your business to find out where you can use Artificial Intelligence. Depending on your requirements, you can benefit from inventory management or chatbots. Look at the return on investment (ROI) for your particular situation. Find the Hidden Business Opportunities. The problem with any emerging technology is that it can fail. So you might lose your investment. You can take an incremental approach to mitigate risks. Smaller increments will also give you an opportunity to course correct faster. Get Ahead of the Business Risks. Building an AI infrastructure is expensive and time-consuming. It requires developers to build the systems, data scientists to train neural networks, data mappers to label the raw data, and necessary machines to process the algorithms. For most businesses, hiring 100s of data mappers or establishing their own AI data centers isn’t practical. So finding reliable managed service providers can help you get the benefits of AI without heavily investing in expensive infrastructure. Find Reliable AI Partners Who Will Support Your Growth. You can only learn by trying. It’s normal for organizations to get things wrong. If you start implementing solutions, you will learn from your mistakes. Your experience will give you an advantage over your competitors. Gain Experience Through Being an AI Practitioner.

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