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AI in robotics: Problems and solutions


We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Robotics is a diverse industry with many variables. Its future is filled with uncertainty: nobody can predict which way it will develop and what directions will be leading a few years from now. According to the International Federation of Robotics data, 3 million industrial robots are operating worldwide – the number has increased by 10% over 2021. The global robotics market is estimated at $55.8 billion and is expected to grow to $91.8 billion by 2026 with a 10.5% annual growth rate.

NLP and Sentiment Analysis for Beginners


This program will give you in-depth knowledge of how NLP and sentiment analysis helps you determine the emotional meaning of communications. This program will give you in-depth knowledge of how NLP and sentiment analysis helps you determine the emotional meaning of communications. You'll learn how NLP applications and Sentiment analysis help you to read, understand, and decode human words in a valuable manner. This program will walk you through different NLP algorithms, and you'll get practical knowledge on how to write code in Python, and implement NLP algorithms. This program will help you learn NLP, Sentiment Analysis, and Deep Learning from basic to advance.

IBM focuses on shortage of AI talent in IT and security


IBM has been warning about the cybersecurity skills gap for several years now and has recently released a report on the lack of artificial intelligence (AI) skills across Europe. The company said in a Friday email to SC Media that cybersecurity has been experiencing a significant workforce and skills shortage globally, and AI can offer a crucial technology path for helping solve it. "Given that AI skillsets are not yet widespread, embedding AI into existing toolsets that security teams are already using in their daily processes will be key to overcoming this barrier," IBM stated in the email. "AI has great potential to solve some of the biggest challenges facing security teams -- from analyzing the massive amounts of security data that exists to helping resource-strapped security teams prioritize threats that pose the greatest risk, or even recommending and automating parts of the response process." Oliver Tavakoli, CTO at Vectra, said the potential of machine learning (ML) and AI materially helping in the pursuit of a large set of problems across many industries has created an acute imbalance in the supply and demand of AI talent.

Finding AI's low-hanging fruit


We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Delivering AI solutions from the test bed to production environments will probably be the key focus for the enterprise throughout the next year or longer. But organizations should be cautious not to push AI too far too fast, despite the pressure to keep up with the competition. This often leads to two key problems. First, it pushes inadequate solutions into environments where they are quickly overwhelmed and this leads to failure, disillusionment and mistrust from the user base that ultimately inhibits adoption. The AI industry is not helping anything with its stream of promises that their solutions offer complete digital autonomy and transformative experiences.

MIT, Harvard scientists find AI can recognize race from X-rays -- and nobody knows how - The Boston Globe


A doctor can't tell if somebody is Black, Asian, or white, just by looking at their X-rays. The study found that an artificial intelligence program trained to read X-rays and CT scans could predict a person's race with 90 percent accuracy. But the scientists who conducted the study say they have no idea how the computer figures it out. "When my graduate students showed me some of the results that were in this paper, I actually thought it must be a mistake," said Marzyeh Ghassemi, an MIT assistant professor of electrical engineering and computer science, and coauthor of the paper, which was published Wednesday in the medical journal The Lancet Digital Health. "I honestly thought my students were crazy when they told me."

'Big Hero 6' sequel 'Baymax!' hits Disney on June 29th


Baymax!, the Disney sequel to 2014's Big Hero 6, will debut on June 29th. Disney shared the release date on Friday, alongside a new trailer showing the loveable healthcare robot from the film attempting to help the citizens of San Fransokyo. "In each of our six episodes, Baymax just wants to help someone--and a lot of times they don't want to be helped," said creator Don Hall. "He sets out to fix a physical issue that he's identified, and in the process, gets to a deeper, more emotional place and can be almost transformative in that role." Baymax! is the second Big Hero 6 sequel following Big Hero 6: The Series, a 2D animated show that ran for three seasons on the Disney Channel.

[NLP] Pos Analysis


To allow a machine to understand human language, the components of each sentence must be categorized. One of the basic classification systems is the POS (part-of-speach), natively integrated into the nltk library. These tags give each component of the sentence a grammatical meaning. Let's do a test with a short script. To run it you need the pip package and the downloader for NLTK.

AI transformer models touted to help design new drugs


Special report AI can study chemical molecules in ways scientists can't comprehend, automatically predicting complex protein structures and designing new drugs, despite having no real understanding of science. The power to design new drugs at scale is no longer limited to Big Pharma. Startups armed with the right algorithms, data, and compute can invent tens of thousands of molecules in just a few hours. New machine learning architectures, including transformers, are automating parts of the design process, helping scientists develop new drugs for difficult diseases like Alzheimer's, cancer, or rare genetic conditions. In 2017, researchers at Google came up with a method to build increasingly bigger and more powerful neural networks.

Computer Vision Pipeline with Kubernetes


We produce a multitude of attributes (characteristics attached to an entity -- building, parcel, etc.) using various sources such as aerial imagery. The idea is to build Deep Learning models from a few thousand buildings using in-house-tagged labels or existing labels from open data. In a second step, the models are deployed on the whole French territory, which represents more than 35 million images to process (i.e. 4 TB of data to deal with). This second step is the focus of this post. The challenge is to be able to infer at low cost and in a short amount of time, (less than a day).

Top 10 AI-Generated Images by DALL-E 2 - Simplified


OpenAI, a San Francisco Artificial Intelligence company closely affiliated with Microsoft, launched an A.I. system and neural network in January 2021 known as DALL-E. Named using a pun of the surrealist artist Salvador Dalí and Pixar's famous movie WALL-E, DALL-E creates images from text.In this blog, we'll let you in on everything you should know about DALL-E, its variation DALL-E 2, and share ten of the most creative AI-generated images of Dall-E 2. Picture of a dog wearing a beret and a turtleneck generated by the DALL-E 2 image generation software. Now, you may be wondering what DALL-E is all about. It's an AI tool that takes a description of an object or a scene and automatically produces an image depicting the scene/object. DALL-E also allows you to edit all the wonderful AI-generated images you've created with simple tools and text modifications.