If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Are you passionate about neurotechnologies? Whether you are a student, a researcher or a professional working in this fascinating field, this event is for you! NeuroTechX - the largest global community focused on neurotechnology - is putting together an exciting networking event where you'll be provided with a yearly recap of everything you should know about what happened in neurotechnology in 2019 around the world. Join us for the London version of this exciting event, which will also be happening in many other cities around the world. This event is sponsored by BIOS (bios.health), a full stack neural interface platform that uses AI to decode and encode the signals from the brain to the body, to treat chronic health conditions.
Artificial intelligence offers Britain the opportunity to have a world-leading school system. We have a good education system, but it is not innovative nor exciting. Nor is it in tune with the post-Brexit world. AI is transforming every aspect of the human experience. Britain is making considerable progress in applying it to healthcare, to the professions and to industry, but despite good progress schooling remains the Cinderella of AI.
Technology is evolving continuously, and so are we. In the upcoming years, there will be massive growth in the AI and Machine Learning field. There is already a considerable amount of data to be managed, and with new technological advancements, we can utilize big data in many ways. For that, we have to stay up to date with the latest trends in data science. Data Science is not a single term; it covers a variety of topics and networks, such as the Internet of Things, Deep Learning, AI, etc.
Those are a few examples of challenges a product manager might face when working with artificial intelligence. As someone who has practiced the craft for decades (I founded a company that built an AI-related, algorithmic product and now run product management for applied AI for Xerox at PARC), I want to share some thoughts on what is distinctive about product management for AI. Product managing AI-based applications is still product management, but it requires some additional know-how, and maybe even some magic dust. While the role of product manager has been around since the '30s, the specifics of the job function (the "mini-CEO") have generally been vague. Nevertheless, the popularity of careers in product management have soared in recent years.
Did you know: Robots can now heal themselves? The self-healing AI-based robot is said to be known as one of the most significant engineering breakthroughs to date. Artificial Intelligence offers limitless opportunities regardless of the industry, and AI-based tools can help you achieve higher levels of expertise and keep you ahead of your competition. Many are trying to support the innovation, while only a few of them are able to maintain some level of control over implementing AI in their business. With a huge payoff on the line, it is currently estimated that AI has the power to increase global GDP by 14 percent by 2030, which adds US$15.7 trillion into the global economy.
Anil is Founder and Managing Partner at Unicorn India Ventures an leading Venture Capital Fund focusing on early stage investments in tech focused companies. He has been one of the pioneers of angel investments in India as head of operations and President at Mumbai Angels and Bangalore Angels, leading Angel Investment forums in India. Anil's experience includes corporate management functions in medium and large organizations, early stage investment in start-ups, project management, joint ventures and business development. Anil has helped close over 100 venture-financing deals. Anil serves on the Board of 5 companies and is involved with various incubation centres as mentor in India as well as internationally.
Three members from the legal affairs committee are currently working to ensure the EU is prepared for the legal and ethical aspects of developments in artificial intelligence (AI). Find out more in our interview. German EPP member Axel Voss, the member responsible for issues relating to civil liability regime for artificial intelligence, speaks about how the EU can solve the legal uncertainties created by the use of AI. What problems does the Parliament wants to solve? Although Europe's existing civil liability framework covers most upcoming scenarios, new technologies based on AI will nevertheless expose several unsolved issues.
It is predicted that the precision agriculture market will reach $12.9 billion by 2027. With this increase, there is a need for sophisticated data-analysis solutions that are capable of guiding management decisions in real-time. A new methodology has been developed by an interdisciplinary group at the University of Illinois, and it aims to efficiently and accurately process precision agricultural data. Nicolas Martin is an assistant professor in the Department of Crop Sciences at Illinois and co-author of the study. "We're trying to change how people run agronomic research. Instead of establishing a small field plot, running statistics, and publishing the means, what we're trying to do involves the farmer far more directly. We are running experiments with farmers' machinery in their own fields. We can detect site-specific responses to different inputs. And we can see whether there's a response in different parts of the field," he says.
IBM's Deep Blue wasn't supposed to defeat Chess grandmaster Gary Kasparov when the two of them had their 1997 rematch. Computer experts of the time said machines would never beat us at strategy games because human ingenuity would always triumph over brute-force analysis. After Kasparov's loss, the experts didn't miss a beat. They said Chess was too easy and postulated that machines would never beat us at Go. Champion Lee Sedol's loss against DeepMind's AlphaGo proved them wrong there. Then the experts said AI would never beat us at games where strategy could be overcome by human creativity, such as poker.
We are witnessing a data labeling market explosion: labeling platforms have hit prime time. S&P Global released an October 11 report entitled *Avoiding Garbage in Machine Learning* in which it termed unlabeled data "garbage data" to highlight the importance of labeling in AI. The Economist recently noted that while spending on AI is growing from $38bn this year to $98bn in 2023, only 1 in 5 companies interested in AI has deployed machine learning models because of a shortage of labeled data. This is why "the market for data-labeling services may triple to $5bn by 2023." It is difficult not to notice the abundance of labeling startups being funded of late that are chasing after this market.