It\u2019s all swell and exciting.\u00a0 However, at least for now, AI is as good as the human brain that has created it.<\/p>\r\n
We want to take a deep breath, forget about the hype and theories for just 1 minute and analyze what we have right now and in the near future. Most importantly, we want to give credit to the amazingly smart humans who are building this great technology.\u00a0 It\u2019s humans who are running the show right now, not the robots.\u00a0 Just look at the record-high number of jobs for data scientists and machine learning experts.<\/p>\r\n
Artificial intelligence is not designed to take on tasks it was not meant to.\u00a0 Roomba can vacuum.\u00a0 It could vacuum very well.\u00a0 It could potentially use deep learning to vacuum even better, faster and with higher quality.\u00a0 However, unless programmed to do so by humans, it\u2019s not going to \u201clearn\u201d to do other tasks.<\/p>\r\n
Machines are great at routine, repetitive tasks.\u00a0 They are great at math: computing and aggregation.\u00a0 And they are super-fast.<\/p>\r\n
But for now, there is no one better than humans in humility, social skills and intuition.\u00a0 These are important characteristics anywhere, but especially in healthcare, when you are often dealing with life-and-death situations.<\/p>\r\n
Another thing is the data.\u00a0 Most of the world data is garbage, virtual garbage:<\/p>\r\n
https:\/\/www.technologyreview.com\/2021\/04\/01\/1021619\/ai-data-errors-warp-machine-learning-progress\/<\/a><\/p>\r\n As of now, in 2021, the best machine learning technology is making errors even with \u201cgood\u201d data, as in the panda-vs-gibbon example: With the \u201cdirty\u201d data, which is the majority of the world data, how would one expect AI to learn?<\/p>\r\n Data scientists are aware of these issues.\u00a0 We believe humility is what distinguishes excellent data scientists from world-class data scientists. What we’ve noticed in our WellAI research is that even textbook model validation on \u201cclean\u201d data could be very different from real life.\u00a0 The latest and the shiniest algorithms could suffer from ‘overparameterization’ and ‘overfitting’. \u00a0Simpler is often better, as the MIT Technology Review article correctly points out.<\/p>\r\n As for the data itself, the quality of the medical data is especially important as we are dealing with people’s health and people’s lives. Ironically, healthcare suffers from erroneous and biased data, partly because the so-called Big Pharma and Big Tech are profit-driven and could bias medical and clinical studies in a certain direction, often in a very subtle, unnoticeable way.<\/p>\r\n In WellAI’s educational live sessions, we very often emphasize the importance of humility and not taking any AI-generated outcomes for granted. For example, in this short clip of the lecture given in February 2021, WellAI’s co-founder and a member of the IFCC’s working group on AI & Genomics Daniel Satchkov talks about being humble about AI: Daniel points out that both the terms AI and ‘learning’ are complete misnomers and quite apart from data problems. These algorithms are not intelligent and do not learn.\u00a0 Adversarial examples such as the panda-vs-gibbon one mentioned above have put to rest any generalized learning pretense long ago.\u00a0 Data problems described in the MIT Technology Review article must be solved.\u00a0 That is a given. But the bigger problem is an overreach of pitching these algorithms as being able to learn and solve whole classes of problems that they simply cannot. Machine learning is great for extracting patterns from data and human behavior, and it could be a great help in automating some important tasks and freeing up humans. But beyond that, it is hype and snake oil.<\/p>\r\n Don\u2019t get us wrong \u2013 we are super excited about AI.\u00a0 We are an artificial intelligence company, and we love helping humanity with our AI expertise.\u00a0 We do believe there is a place for AI, and there is a place for humans.\u00a0 And the best and most productive scenario (for the healthcare system in particular) is when the AI and the humans are working together, utilizing their specific skills and talents.<\/p>\r\n At WellAI, we use artificial intelligence for what we believe artificial intelligence is supposed to be used: quick, efficient, cheap and accurate information access to the knowledge of 30+ million medical studies.<\/p>\r\n Try WellAI for yourself.\u00a0 The price for our flagship voice-guided digital health assistant is still super low at $39.99 per year (which is equivalent to $3.33 per month).\u00a0 You can make your purchase directly on our website https:\/\/wellai.health<\/a>\u00a0or at the\u00a0App Store<\/a>\u00a0or on\u00a0Google Play<\/a>\u00a0on your smartphone.<\/p>\r\n If you want to learn more about this breakthrough technology that is meant to provide unique access to scientific health information to everyone and to dramatically improve your health knowledge, please schedule a 1-on-1 demo:<\/p>\r\n Stay healthy!\u00a0 Be knowledgeable about your health!<\/p>\r\n\r\n
https:\/\/towardsdatascience.com\/breaking-neural-networks-with-adversarial-attacks-f4290a9a45aa<\/p>\r\n
https:\/\/www.linkedin.com\/posts\/spolevikov_pandemic-health-ai-activity-6767101066002743296–mHf<\/a><\/p>\r\n
So sleep well.\u00a0 Skynet is not sending a Terminator to kill the human race.\u00a0 At least not yet\u2026<\/p>\r\n\r\n
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