Artificial intelligence, including machine and deep learning, are revolutionizing the drug discovery and development process – bringing with it unprecedented levels of speed and efficiency. Meanwhile, San Francisco's unparalleled tech ecosystem provides Bay Area biotechs proximity to myriad opportunities for AI integration. Join us to explore the latest technology and business trends in this burgeoning sector through the lens of some of San Francisco's top life science leaders and rising stars, and learn how AI is impacting investment and partnerships.

PyTorch is helping to power a new generation of AI-enhanced farming machines. For farmers, weeds pose a very real threat to the health of crops at a time when global population growth is raising food demand while also making resources such as land and water increasingly scarce. Seeking solutions to helping farmers produce more food with fewer resources, California-based Blue River Technology, a subsidiary of John Deere, has turned to artificial intelligence and robotics technology. The company's See & Spray robotic farming machine combines machine learning (ML) and computer vision to identify weeds among crops in real time and to treat weeds while leaving crops unharmed -- giving farmers a more consistent, precise, and efficient means of weeding crops. As the See & Spray machine moves through a field, it collects images of crops and weeds through the use of a high-resolution camera array.

In July 2015, two founders of DeepMind, a division of Alphabet with a reputation for pushing the boundaries of artificial intelligence, were among the first to sign an open letter urging the world's governments to ban work on lethal AI weapons. Notable signatories included Stephen Hawking, Elon Musk, and Jack Dorsey. Last week, a technique popularized by DeepMind was adapted to control an autonomous F-16 fighter plane in a Pentagon-funded contest to show off the capabilities of AI systems. In the final stage of the event, a similar algorithm went head-to-head with a real F-16 pilot using a VR headset and simulator controls. The AI pilot won, 5-0.

Analysing four photographs of a person's face from selfies could be a cheap and simple way of detecting heart disease, according to a new study. The study, published in the European Heart Journal, is the first to show that it is possible to use a deep learning computer algorithm to detect coronary artery disease (CAD). The researchers say it has the potential to be used as a screening tool that could identify possible heart disease in people in the general population or in high-risk groups, who could be referred for further clinical investigations. "To our knowledge, this is the first work demonstrating that artificial intelligence can be used to analyse faces to detect heart disease. It is a step towards the development of a deep learning-based tool that could be used to assess the risk of heart disease, either in outpatient clinics or by means of patients taking'selfies' to perform their own screening. This could guide further diagnostic testing or a clinical visit," said Professor Zhe Zheng, who led the research and is vice director of the National Center for Cardiovascular Diseases and vice president of Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing.

Artificial Intelligence (AI) has evolved quickly in the last decade, thanks to the development of more powerful machines and scientific methods like deep learning and natural language processing. So, it is not crazy to think that machines could one day replace humans. Already, AI is replacing humans in industries like manufacturing. However, it depends on how people see this technology because it is making our work easier. Instead of lamenting the fact that machines can do now what previously only humans could do, learn more tech skills, and don't fall behind.

This post describes our work and research on the greenScreen.AI. We'll be happy to hear thoughts and comments -On Twitter, Linkedin Throughout the last few years in machine learning, I've always wanted to build real machine learning products. A few months ago, after taking the great Fast.AI deep learning course, it seemed like the stars aligned, and I have the opportunity: The advances in deep learning technology permitted doing many things that weren't possible before, and new tools were developed and made the deployment process more accessible than ever. In the aforementioned course, I've met Alon Burg, who is an experienced web developer, an we've partnered up to pursue this goal. Together, we've set ourselves the following goals: Our early thoughts were to take on some medical project, since this field is very close to our hearts, and we felt (and still feel) that there is an enormous number of low hanging fruits for deep learning in the medical field.

This section will cover what the next word prediction model built will exactly perform. The model will consider the last word of a particular sentence and predict the next possible word. We will be using methods of natural language processing, language modeling, and deep learning. We will start by analyzing the data followed by the pre-processing of the data. We will then tokenize this data and finally build the deep learning model.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Building machine learning systems differs from traditional software development in many aspects of its lifecycle. Established software methodologies for testing, debugging and troubleshooting result simply impractical when applied to machine learning models.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Last week, many tech publications broke news about a blog generated by artificial intelligence that fooled thousands of users and landed on top of the Hacker News forum. GPT-3, the massive language model developed by AI research lab OpenAI, had written the articles. Since its release in July, GPT-3 has caused a lot of excitement in the AI community. Developers who have received early access to the language model have used to do many interesting things, showing just how far AI research has come. But like many other developments in AI, there's also a lot of hype and misunderstanding surrounding GPT-3, and many of the stories published about it misrepresent its capabilities. The blog written by GPT-3 resurfaced worries about fake news onslaughts, robots deceiving humans, and technological unemployment, which have become the hallmark of AI reporting.

Despite the buzz around GPT-3, it is, in and of itself, not AGI. In many ways, this makes it similar to AlphaGo or Deep Blue; while approaching human ability in one domain (playing Chess/Go, or writing really impressively), it doesn't really seem like it will do Scary AGI Things any more than AlphaGo is going to be turning the Earth into paperclips anytime soon. While its writings are impressive at emulating humans, GPT-3 (or any potential future GPT-x) has no memory of past interactions, nor is it able to follow goals or maximize utility. However, language modelling has one crucial difference from Chess or Go or image classification. By harnessing the world model embedded in the language model, it may be possible to build a proto-AGI.