"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.
With the help of this list, any person who is interested in artificial intelligence or machine learning can feel free to learn all about it. In this course, the instructor is going to talk about the meaning behind the common AI terminology. It includes explanations about neural networks, machine learning, data science, and deep learning. Then the instructor will talk about what AI can and can't do realistically. Similarly, you will also get to understand how to spot opportunities to apply AI to different problems in your own organization.
Note: First 100 subscribers receive a free lifetime subscription. The rise of Artificial Intelligence (AI) is a trend that will have significant implications for your portfolio. Machine Learning (ML) is ground zero for unleashing the potential of AI for businesses, governments, and consumers. Industries and Institutions vary, but the fundamentals stay the same. We'll look to the Joint Artificial Intelligence Center (JAIC) publication, "Understanding AI Technology" by Greg Allen to get a better understanding of Machine Learning.
Artificial Intelligence research at Duke covers everything from health to enhancing photos to machine learning. See what some Duke researchers are doing in the field. During Winter Breakaway, David Carlson, an assistant professor in Civil and Environmental Engineering taught "AI for Everyone," which included an introduction to the math and computations underlying machine learning and artificial intelligence. Duke student brothers worked together in a home office in Tampa to develop a machine learning system to help clinicians spot the telltale'ground glass opacities' in the lung scans of potential Covid patients. Because no two brains are alike, machine learning is being used to help neurosurgeons home in on the precise area where the electrode should go to treat Parkinson's disease with deep brain stimulation.
Back in November, the computer scientist and cognitive psychologist Geoffrey Hinton had a hunch. After a half-century's worth of attempts--some wildly successful--he'd arrived at another promising insight into how the brain works and how to replicate its circuitry in a computer. "It's my current best bet about how things fit together," Hinton says from his home office in Toronto, where he's been sequestered during the pandemic. If his bet pays off, it might spark the next generation of artificial neural networks--mathematical computing systems, loosely inspired by the brain's neurons and synapses, that are at the core of today's artificial intelligence. His "honest motivation," as he puts it, is curiosity. But the practical motivation--and, ideally, the consequence--is more reliable and more trustworthy AI.
IMAGE: Machine learning helps develop optimal antibody drugs. Antibodies are not only produced by our immune cells to fight viruses and other pathogens in the body. For a few decades now, medicine has also been using antibodies produced by biotechnology as drugs. This is because antibodies are extremely good at binding specifically to molecular structures according to the lock-and-key principle. However, developing such antibody drugs is anything but simple.
NVIDIA and Cloudflare announced a partnership that will "put AI into the hands of developers everywhere." Working with NVIDIA, Cloudflare will offer artificial intelligence (AI) tools to developers on top of its workers developer platform, making it "easier and faster for developers to build the types of applications that will power the future all within a platform." On the Cloudflare blog, it claimed that it is faster and 75% less expensive than AWS Lambda. Cloudflare also announced it will support TensorFlow, the open source tool for testing machine learning models, so developers will have access to familiar tools to help build applications before deploying on Cloudflare's network. The announcement said the intent of the partnership is to "bring AI to the edge at scale."
AI learns from seen data to make predictions about unseen data. What is utterly remarkable is that prediction can underpin extraordinary creativity and mimicry. These developments have the potential to unleash an explosion of scale creativity -- delivering content design and production tools into the hands of the mass market that have hitherto only been available to large corporations with hefty budgets. Even now -- when we are still in the infancy of AI media generation -- there are demos, apps and subscription-based services to faceswap individuals into movies (see Zao), turn rough sketches into photorealistic images (try the GauGAN demo here), convert one voice into another (see Respeecher), personalise marketing videos (try the Synthesia demo here), age- and emotion-alter images (see Photoshop's new Neural Filters), generate face-synched videos of new or translated scripts (see Canny AI), play a video game with characters speaking any of 10 face-synched languages (see Cyberpunk 2077), and play a text-based adventure game with endless dialogue generated by AI (try out the free version of AI Dungeon here). Moreover, the same AI techniques will spawn new applications in a wide range of fields: advertising, architecture, interior design, gaming, song-writing, web design, education, even software development and pure mathematics -- in fact anywhere where structured or constrained creativity is key.
India is a breeding ground for many industries. The increase in educated population and the run towards growth has unraveled technology into the country. Today, technology is a core element of growth in the Indian ecosystem. While well-established companies are embracing artificial intelligence for further improvement, Indian start-ups are ballooning like never before. Fortunately, technology-based Indian start-ups landscape has evolved to become the 3rd largest in the world.
Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering? Today, you can stop imagining, and start doing. This course will teach you the core fundamentals of financial engineering, with a machine learning twist. We will learn about the greatest flub made in the past decade by marketers posing as "machine learning experts" who promise to teach unsuspecting students how to "predict stock prices with LSTMs". You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense.
Retailers are now applying AI, ML, and robotics in significant parts of the value chain. Above all, AI technologies could eliminate many manual activities in assortments, promotions, and supply chains. The three most remarkable opportunities in the short to medium term are promotions, arrangement, and replenishment. Significant retailers are trying different things with AI around these areas. "Digital native" e-commerce organizations are driving the way, using AI to anticipate trends, optimize advanced warehousing and logistics, set costs, and customize advancements and promotions.