Some of today\u2019s smartest people are scared of smart machines.\n\n\nIn a BBC interview, experimental physicist Stephen Hawking said: \u201cI think the development of full artificial intelligence could spell the end of the human race.\u201d Entrepreneur and visionary Elon Musk was more pointed. \u201cWith artificial intelligence, we are summoning the demon,\u201d he said.\n\n\nEarlier this month, Musk announced the founding of a non-profit AI research company, called Open AI, to \u201cadvance digital intelligence in the way that is most likely to benefit humanity as a whole,\u201d according to the company\u2019s mission statement.\n\n\n\n\n\n \nAlso on CIO.com\n\n5 interview questions for big data engineers\u2026 and how to answer them\nMove over CISO: The Chief Data Officer may be sharing part of your job\n11 big data certifications that will pay off\n\n\n\n\n\nA few weeks earlier IBM began promoting SystemML (machine learning), a proprietary machine-learning technology under an open-source license, available through the Apache Software Foundation. SystemML is built to recognize patterns in Big Data.\u00a0 It is designed to work with Spark software, which helps process data as they arrive from multiple sources, such as fitness trackers or, ultimately, EMR systems.\n\n\nBoth efforts are aimed at making AI safer.\u00a0 Louis Rosenberg may have the safest idea of all.\u00a0 \u00a0Rather than create an intelligence that \u201cwill not share our interests and would likely be as foreign from us as an alien intelligence,\u201d Rosenberg suggests amplifying human intelligence with an algorithm that turns groups of people into super experts.\n\n\nRosenberg calls this approach swarm AI.\u00a0 Rather than taking people out of the decision making loop, it makes them a critical part of it.\u00a0 Take the humans out. And the AI. Stops.\n\n\nRosenberg\u2019s algorithm, a product of his company called Unanimous AI, brings people together. It leverages their intuitions, emotions, sensibilities \u2013 and above all, knowledge \u2013 to draw conclusions that are better than any one expert.\u00a0\n\n\nThis approach may be especially suited to medicine and health IT, which could be among the first to feel \u00a0the effects of AI.\u00a0 Learning machines have been proposed for examining medical records, sharing data among information systems, and drawing insights that can help physicians make better decisions in the diagnosis and treatment of patients. IBM is grooming its Watson Health to help physicians make diagnoses.\u00a0 A San Francisco startup called Enlitic is implementing a deep learning algorithm in Australian and Asian imaging clinics to help physicians spot patterns of disease in medical images.\n\n\nThese systems are based on conventional AI, which depends on machine learning.\u00a0 Rosenberg is promoting swarm artificial intelligence as an alternative, one that has all the advantages but none of the drawbacks of conventional AI. It may, in fact, be the only kind of artificial intelligence that has a prayer of being approved by the FDA in the foreseeable future .\n\n\n\u00a0\u201cI think the people who are really excited about conventional AI systems underestimate what doctors are really doing (when they make diagnoses). There is more to it than just simple rule-based decisions,\u201d Rosenberg says. \u201cWith swarm AI, we are trying to have the benefits of amplified intelligence while keeping human sensibilities and human values and human interests deeply ingrained in the system as opposed to just replacing them.\u201d\n\n\nEarly tests of swarm AI for medical diagnosis have been promising. \u00a0In one study, a collective intelligence of radiologists proved superior when interpreting mammograms, reducing false positives and false negatives. This swarm AI overcame \u201cone of the fundamental limitations to decision accuracy that individual radiologists face,\u201d the authors concluded in a peer-reviewed paper published by the Public Library of Science. Their study demonstrated the collective or swarm intelligence could improve mammography screening and has the potential to improve many other types of medical decision-making, \u201cincluding many areas of diagnostic imaging.\u201d\n\n\nIn another study, a dozen radiologists increased their ability to correctly diagnose skeletal abnormalities.\u00a0 The researchers reported at the ninth international conference on swarm intelligence that the \u201calgorithm\u2019s accuracy in distinguishing normal vs. abnormal patients was significantly higher than the radiologists\u2019 mean accuracy.\u201d\n\n\nYou can argue that the real world doesn\u2019t have the luxury of bringing groups of radiologists together to develop a consensus on every case.\u00a0 But swarm AI wouldn\u2019t be needed for every case, Rosenberg says.\n\n\nRoutine cases could be handled by individual physicians who, when stumped by a complex case, could tag it for later examination by swarm AI. \u00a0This would improve diagnosis, while empowering team members.\n\n\n\u201cThese swarms would help the group come to a decision, as opposed to just taking a vote where the vote might just reveal the differences in the group,\u201d Rosenberg says.\u00a0\n\n\nTo promote swarm AI, Rosenberg last year founded Unanimous AI, a company created with the goal of enabling groups to \u201cthink together\u201d. With this application of swarm AI, Rosenberg borrows a page from nature\u2019s playbook wherein species accomplish more by participating in flocks, schools, colonies and swarms than they can individually.\u00a0 Unanimous AI offers the unique infrastructure, he says, by which people can form intelligent swarms.\u00a0\n\n\n\u00a0\u201cWe know that groups are smarter than individuals and we also know that nature has addressed this by having swarms (of animals) make optimal decisions,\u201d he says.\u00a0 \u201cWe are just connecting people in a way that can harness their diverse perspectives and opinions and knowledge.\u201d\n\n\nSwarm AI is distinct from voting in that once members of a group have cast their votes, the process is over.\u00a0 Rather than polling members, swarm AI uses a cyber \u201cpuck\u201d that members of the group constantly pull or push. The puck moves in real-time on the computer screens viewed by the group toward or away from different possibilities. It does so depending on the beliefs and conviction of group members about the correctness, for example, of a diagnosis.\n\n\n\u201cThey are all at the same time watching the members of the group express their conviction, where the group converges on the decision that optimizes their collective will,\u201d Rosenberg says.\n\n\nThe potential for improved analyses without the risks of machine intelligence make swarm AI an attractive alternative to machine learning. Now in beta testing, swarm AI could emerge commercially as early as next year.