- When Unanimous AI developed UNU in 2015, the goal was to create artificial intelligence (AI) systems that “keep people in the loop,” amplifying human intelligence instead of replacing it.
- Unanimous AI’s March Madness bracket was able to beat all but three percent of ESPN brackets across the country on the first day of the tournament.
- The technology makes use of the collective intelligence of people — combining “knowledge, insights, and intuitions,” as Unanimous AI puts it — to develop a kind of artificial intelligence that’s inherently human.
- As Unanimous AI explains, “We empower people to act as ‘data processors’ that come together online and form an intelligent system, connected by AI algorithms.
- One day, perhaps we’ll be able to combine the intelligence of Watson with that of a swarm of medical professionals to improve healthcare, or combine the insights of an investment-making AI with a swarm of finance experts.
The collective intelligence is killing it on ESPN.
Continue reading “March Madness: A Swarm Intelligence Is Predicting the Future”
- Artificial intelligence (AI) in the workplace is becoming more and more common all over the world, in various industries.
- Home News 5 Ways in Which Artificial Intelligence Will Change an Organization
- Intelligent Machines Need to be Treated as Colleagues: Trust AI to make the right decision.
- As AI takes over administration work it’s important for management to stay creative in order to stay successful.
- If managers learn to embrace them and work with them they will spend less time on meaningless tasks and more on the important aspects of running a business.
Artificial intelligence (AI) in the workplace is becoming more and more common all over the world, in various different industries. Not only do AI systems save
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- Those who are able to train faster and deploy AI models that are computationally and energy efficient are at a significant advantage.
- Some have rebranded AI as “cognitive computing” or “machine intelligence”, while others incorrectly interchange AI with “machine learning”.
- If we want AI systems to solve tasks where training data is particularly challenging, costly, sensitive, or time-consuming to procure, it’s important to develop models that can learn optimal solutions from less examples (i.e. one or zero-shot learning).
- Deep learning models are notable for requiring enormous amounts of training data to reach state-of-the-art performance.
- Without large scale training data, deep learning models won’t converge on their optimal settings and won’t perform well on complex tasks such as speech recognition or machine translation.
Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.
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- XNOR.ai frees AI from the prison of the supercomputer
- AI software is figuring out how to best humans at designing new AI software
- Posted 22 hours ago by Darrell Etherington ( @etherington )
- MIT Media Lab is open-sourcing its own efforts to create learning software from other machine learning programs, and this should help with industry-wide efforts to make this a practical way to create new software.
- Kristen Stewart co-authored a paper on style transfer and the AI community lost its mind
Who programs the programmers? Soon enough, it might not be people behind the development of advanced machine learning and artificial intelligence tech, but..
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- A recent Forrester survey of business and technology professionals found that 58% of them are researching AI, but only 12% are using AI systems.
- Concerns about AI stealing jobs are nothing new but we anticipate deeper, more nuanced conversations on what AI will mean economically.
- Expect to hear (a little) less about malevolent AI taking over the world and more about the economic impacts of AI.
- Most AI systems are black boxes -and immensely complex.
- Watch highlights covering artificial intelligence, machine learning, intelligence engineering, and more.
From tools, to research, to ethics, Ben Lorica looks at what’s in store for artificial intelligence in 2017.
Continue reading “7 AI trends to watch in 2017”
- Google’s DeepMind , the AI system that famously defeated the world champion of the board game Go earlier this year, and OpenAI , an AI collaboration from Elon Musk and others, are making their software platforms available to researchers, developers, and anyone else who wants to use them.
- Elon Musk and Google Want You to Make Their Artificial Intelligence Smarter
- Open sourcing an AI system allows it to access troves of new learning data, so the more people who use it, the stronger it becomes.
- Developers could conceivably use these platforms to make the AI in their own games and apps smarter.
- The two platforms both use deep learning, a form of machine learning that allows AI systems to grow smarter.
Musk’s OpenAI and Google’s DeepMind are opening their platforms to the public.
Continue reading “Elon Musk and Google Want You to Make Their Artificial Intelligence Smarter”
- AI systems must be built from the get-go to operate in trust-based partnerships with people.
- We also expect AI systems to pervasively support the decisions we make in our professional and personal lives in just a few years.
- Bias could be introduced into an AI system through the training data or the algorithms.
- Trust of AI systems will be earned over time, just as in any personal relationship.
- But most experts believe that by thoroughly testing these systems, we can detect and mitigate bias before the system is deployed.
Ethics and accountability.
Continue reading “What It Will Take for Us to Trust AI”