"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
It's been a while since I last posted a new entry on the TorchVision memoirs series. Thought, I've previously shared news on the official PyTorch blog and on Twitter, I thought it would be a good idea to talk more about what happened on the last release of TorchVision (v0.12), what's coming out on the next one (v0.13) My target is to go beyond providing an overview of new features and rather provide insights on where we want to take the project in the following months. TorchVision v0.12 was a sizable release with dual focus: a) update our deprecation and model contribution policies to improve transparency and attract more community contributors and b) double down on our modernization efforts by adding popular new model architectures, datasets and ML techniques. Key for a successful open-source project is maintaining a healthy, active community that contributes to it and drives it forwards.
One of the easiest, and yet also the most effective, ways of analyzing how people feel is looking at their facial expressions. Most of the time, our face best describes how we feel in a particular moment. This means that emotion recognition is a simple multiclass classification problem. We need to analyze a person's face and put it in a particular class, where each class represents a particular emotion. In Python, we can use the DeepFace and FER libraries to detect emotions in images.
I had seen the Edge Impulse development platform for machine learning on edge devices being used by several boards, but I hadn't had an opportunity to try it out so far. So when Seeed Studio asked me whether I'd be interested to test the nRF52840-powered XIAO BLE Sense board, I thought it might be a good idea to review it with Edge Impulse as I had seen a motion/gesture recognition demo on the board. It was quite a challenge as it took me four months to complete the review from the time Seeed Studio first contacted me, mostly due to poor communications from DHL causing the first boards to go to customs' heaven, then wasting time with some of the worse instructions I had seen in a long time (now fixed), and other reviews getting in the way. But I finally managed to get it working (sort of), so let's have a look. Since the gesture recognition demo used an OLED display, I also asked for it and I received the XIAO BLE board (without sensor), the XIAO BLE Sense board, and the Grove OLED Display 0.66″.
By using this framework, anyone can build neural networks with graphs. This also depicts operations as nodes. PyTorch is one of the most important frameworks in artificial intelligence. However, it is super adaptable in terms of integrations and languages. It was released by Facebook's AI research lab. This also acts as an open source library useful in deep learning, computer vision and natural language processing software. Another feature is its greater affinity with iOS as well as Android etc. It uses debugging tools like IPDB and PDB.
Explains Nikola Konstantinov of Switzerland's ETH Zürich, "Fairness in AI is about ensuring that AI models don't discriminate when they're making decisions, particularly with respect to protected attributes like race, gender, or country of origin." As artificial intelligence (AI) becomes more widely used to make decisions that affect our lives, making certain it is fair is a growing concern. Algorithms can incorporate bias from several sources, from the people involved in different stages of their development to modelling choices that introduce or amplify unfairness. A machine learning system used by Amazon to pre-screen job applicants was found to display bias against women, for example, while an AI system used to analyze brain scans failed to perform equally well across people of different races. "Fairness in AI is about ensuring that AI models don't discriminate when they're making decisions, particularly with respect to protected attributes like race, gender, or country of origin," says Nikola Konstantinov, a post-doctoral fellow at the ETH AI Center of ETH Zürich, in Switzerland. Researchers typically use mathematical tools to measure the fairness of machine learning systems based on a specific definition of fairness.
The use of machine learning to perform blood cell counts for diagnosis of disease instead of expensive and often less accurate cell analyzer machines has nevertheless been very labor-intensive as it takes an enormous amount of manual annotation work by humans in the training of the machine learning model. However, researchers at Benihang University have developed a new training method that automates much of this activity. Their new training scheme is described in a paper published in the journal Cyborg and Bionic Systems on April 9. The number and type of cells in the blood often play a crucial role in disease diagnosis, but the cell analysis techniques commonly used to perform such counting of blood cells--involving the detection and measurement of physical and chemical characteristics of cells suspended in fluid--are expensive and require complex preparations. Worse still, the accuracy of cell analyzer machines is only about 90 percent due to various influences such as temperature, pH, voltage, and magnetic field that can confuse the equipment.
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.
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.
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.
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.