Arguably the most consequential technological transformation of our lifetimes is coming faster than we think -- and we're not adequately prepared. Why it matters: Artificial intelligence is rapidly improving at human-like tasks, like language and reasoning. Case in point: Five years ago, AI's big win was that AlphaGo -- a machine built by Google -- beat humans at the game Go, which was one of the last games where humans still had the upper hand. What's happening: We asked the top experts in our newsroom exactly what AI can and can't do -- and how we should be thinking smartly about it. Yes, AI is getting smarter.

The full episode of SaugaTalks with Prashant Natarajan, Product-Led Growth at H2O.ai, Best-Selling Author of the “Demystifying…” Series, Co-Faculty, Stanford Medicine TimeCodes:00:00 AI Human Decisions Making and Machine Decision Making, Separating Hype from Reality04:33 AI Strategy is about IMPA...

Our present human society is a product of millions of years of biological evolution and thousands of years of social evolution. We make beliefs about people or things based on our accumulated knowledge. In such a scenario, it is quite natural that some of our beliefs are prejudiced because, at times, we do not have enough information. Gordon Allport defines "prejudice" as a "feeling, favorable or unfavorable, toward a person or thing, prior to, or not based on, actual experience." It is often said that prejudices exist and will continue to exist.

Artificial Intelligence (AI) is a term so overused and misunderstood that it has lost its meaning. As a data scientist who spends a significant amount of time thinking about how and where to apply machine learning to challenges in life sciences and the pharmaceutical industry, when the majority of individuals say AI, what I feel they really mean is technology that can simulate human intelligence. By that definition, everything from a simple calculator to a recurrent generative adversarial network (and beyond) could be considered AI. But thinking of AI as an algorithmic method may not be the best way to recognize its value. Economist Theodore Levitt once said, "No one wants a drill. What they want is the hole."

As artificial intelligence drives a fourth industrial revolution, fears and doubts about AI are pervasive. In the first industrial revolution, machines began replacing manual labor, and there were human concerns over the change. At hand today, in the fourth industrial revolution, are rational and irrational fears. With any new radical thinking and ideas, disruption in the status quo and an influx of unknown information -- people have fear. Great idea -- but it was scary because it was unknown.

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Thursday, October 10th at 10am PDT / 1pm EDT Machine Learning (ML) isn't just a buzzword anymore -- it's affecting how we communicate, shop, live and respond to critical IT incidents. While some IT and engineering leaders are concerned that implementing ML in their incident response processes will render their employees obsolete, others simply don't trust a machine to automate sensitive work. However, when implemented correctly, we believe ML can enhance -- not replace -- the work your teams are already doing, without requiring much or any effort on your part. To learn how, join us for a live webinar with the VictorOps Head of Data Science and resident Machine Learning expert, Will Stanton. We'll focus on: Demystifying AI, Machine Learning and AIOps: Definitions, anecdotes and fun facts to help you understand the technologies shaping the future of engineering and IT The meaning of "human-centered" ML: How VictorOps delivers insights to the end user exactly when and where they need it -- all while keeping them in complete control What makes ML work well: Ideal use cases for getting started with Machine Learning in incident response, effortlessly Demystifying AI, Machine Learning and AIOps: Definitions, anecdotes and fun facts to help you understand the technologies shaping the future of engineering and IT The meaning of "human-centered" ML: How VictorOps delivers insights to the end user exactly when and where they need it -- all while keeping them in complete control What makes ML work well: Ideal use cases for getting started with Machine Learning in incident response, effortlessly The meaning of "human-centered" ML: How VictorOps delivers insights to the end user exactly when and where they need it -- all while keeping them in complete control

Please join us for a special webinar on demystifying AI for insights management. Sjoerd Koornstra, Partner at the House of Insights, kicked off this special webinar on demystifying AI for insights management. Sjoerd talked about different types of machine learning and gave a presentation on how it has been used to combine information from different databases to create insights on almost 50, 000 potential products, utilizing existing knowledge and saving enormous amounts of money and effort. Martin Rückert, Chief Artificial Intelligence Officer at Market Logic, also joined the webinar to show how AI makes life easier for insights managers by delivering instant answers to questions from past research and cutting out the noise. A live Q&A session followed the presentations.

Artificial Intelligence (AI), one of the most thrilling and transformative opportunities of our time, is the topic de jour lately with every intersection of intellectual discourse and business discussions sounding in on the potential perks, risks and dangers. Africa's tech ecosystem, one of the most exciting in the world right now, has a growing community of African start-ups that are keen on developing solutions for African problems using this emerging technology. The most desired business outcomes from AAI are: to improve/develop new products/ services; to achieve cost efficiencies, streamline business operations; and accelerate decision making. Enterprises that have enabled AI have reported increased operational efficiency, making faster, more informed decisions and innovating new products and services. To date, strong AI has not yet come into existence, it's still hypothetical hence it exists in the dreams of research scientists and imagination of science fiction writers.

In today's data driven business world, catchphrases like data analytics, data science (DS), artificial intelligence (AI), machine learning (ML) and deep learning (DL) are terms swirling around boardroom discussions. These digital concepts are increasingly becoming imperative for CIOs and IT leaders in critical decision-making process. Often times these terminologies are loosely or interchangeably referred, without deciphering the real meaning and how individually and collectively they impact businesses. In order to exploit the true potential of these technologies, it is critical for companies to demystify the ambiguity surrounding them. In this blog, we attempt to demystify the terminologies, explain the comprehensiveness of what data science and data analytics is, what artificial intelligence (AI) embodies, and how technologies like Machine Leaning (ML) and Deep Learning (DL) are evolving fast, stimulating AI adoption on a broader scale.