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According to Gartner, AI applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decision-making, and take action. In essence, the concept of AI centres on enabling computer systems to think and act in a more'human' way, by learning from and responding to the vast amounts of information they're able to use. AI is already transforming our everyday lives. From the AI features on our smartphones such as built-in smart assistants, to the AI-curated content and recommendations on our social media feeds and streaming services. As the name suggests, machine learning is based on the idea that systems can learn from data to automate and improve how things are done – by using advanced algorithms (a set of rules or instructions) to analyse data, identify patterns and make decisions and recommendations based on what they find.
One of the coolest parts of building machine learning models is sharing the models we built with others. No matter how many models you've built, if they stay offline, only very few people will be able to see what you've accomplished. This is why we should deploy our models, so anyone can play with them through a nice UI. Flask is a Python framework that lets us develop web applications easily. After following this guide, you'll be able to play with a simple machine learning model in your browser as shown in the gif below.
Steven J. Vaughan-Nichols, aka sjvn, has been writing about technology and the business of technology since CP/M-80 was the cutting edge, PC operating system; 300bps was a fast Internet connection; WordStar was the state of the art word processor; and we liked it. Linux has long played a role in cars. Some companies, such as Tesla, run their own homebrew Linux distros. Audi, Mercedes-Benz, Hyundai, and Toyota all rely on Automotive Grade Linux (AGL). AGL is a collaborative cross-industry effort developing an open platform for connected cars with over 140 members.
Long ago, I built a hand-written digit recognition web app using Flask and TensorFlow. It was my first ML project as a beginner which didn't end up dying in a notebook, so I think it's worth sharing. This is how it's gonna look: In this tutorial, we will build our digit recognition model using TensorFlow and the MNIST dataset, which contains 70,000 images of hand-written digits 0 to 9, convert it into a TFLite model, and then build the web app. We'll be using Google Colab throughout this guide, because it's the easiest way to get started. We'll use the Keras Datasets API to load our MNIST images, because it makes it extremely easy to load the data.
The latest edition of our flagship learning series on everything in and about data analytics is sure to excite your minds, be prepared for the DataHour on Building your First Chatbot using Open Source Tools. The session will be hosted by Dr. Rachael Tatman- Staff Developer Advocate at Rasa, the world's leading conversational AI platform, that enables enterprises to revamp customer experience with cutting-edge open-source machine learning implementations. In this session, you will be led on an engaging journey of using the open-source platform Rasa, and the lecture will be helmed by an ex-Googler and an instructor at the University of Michigan, Dr. Rachael Tatman. The session is for both freshers and professionals alike who would like to design chatbots to improve the CX for their organisations or simply get hands-on experience with open source tools like Rasa. Chatbots have been around for some time.
As data scientists, we are used to developing and training machine learning models in our favorite Python notebook or an integrated development environment (IDE), like Visual Studio Code (VSCode). Often times, any bugs or performance issues go undiscovered until the application has already been deployed. The resulting friction between app developers and data scientists to identify and fix the root cause can be a slow, frustrating, and expensive process. As AI is infused into more business-critical applications, it is increasingly clear that we need to collaborate closely with our app developer colleagues to build and deploy AI-powered applications more efficiently. As data scientists, we are focused on the data science lifecycle, namely data ingestion and preparation, model development, and deployment.
As knowledge workers including software engineers shifted to remote work during the pandemic, executives expressed a concern that productivity would suffer as a result. The evidence is mixed on this, but in the software industry particularly, remote work exacerbated many of the challenges that employees already faced. According to a 2021 Garden survey, the majority of developers found slow feedback loops during the software development process to be a source of frustration, second only to difficult communication between teams and functional groups. Seventy-five percent said the time they spend on specific tasks is time wasted, suggesting it could be put to more strategic use. In search of a solution to bolster developer productivity, three former Atlassian employees -- Dylan Etkin, Michael Knighten and Don Brown -- cofounded Sleuth, a tool that integrates with existing software development toolchains to provide insights to measure efficiency.
ServiceTitan, which makes operating software for electricians, plumbers and the like, is stepping up its game by developing artificial intelligence of the type normally used by more sophisticated companies to streamline repetitive tasks and bring data to decision-making. ServiceTitan, the software developer for tradespeople such as electricians and plumbers, has moved into artificial intelligence. The Glendale company unveiled Titan Intelligence, or TI, at its Pantheon 2022 conference for its customers, including business owners, managers, IT and finance team members. The event was held at the Los Angeles Memorial Coliseum on April 20-22. Anmol Bhasin, chief technology officer for ServiceTitan, said he previously worked with AI at Salesforce.com Inc. and Groupon Inc., and his goal is to bring the same types of services that those companies offer to the trades.
Chris J. Preimesberger has been researching, reporting and analyzing IT news and trends since 1995, when as editor of an international newsletter, Sun's Hottest, he published an article defining a new protocol called Java. Damage caused by advanced exploits, such as Log4Shell and Spring4Shell, has been widely documented. These came out of nowhere and seemingly crippled many organizations. This happened despite record cybersecurity industry budgets that will clear $146B in 2022. This post from Palo Alto Networks highlights that, based on telemetry, the company observed more than 125 million hits that had the associated packet capture that triggered the signature.