Artificial Intelligence: This Month’s Fine Progress, Real and Potential

From March 6^th to March 20^th, there was a string of articles – two about areas of true AI achievement, one questioning what may or may not have happened earlier this year, and two trying to persuade us about its ability to reach certain lofty heights. 

In the first, we saw how “Taco Bell shows off AI ‘coach’ following massive digital tech investment” (Pilar Arias, Fox Business, March 6^th).  Already, “about 500 Taco Bell U.S. locations have AI voice technology to take drive-thru orders,” up from 100 nine months before, and its Byte software, at least partially in use at 25,000 Taco Bell and KFC restaurants, “is widely scaled and enables operational consistency and restaurant manager efficiency,” including “online and mobile app ordering, point of sale, kitchen and delivery optimization, menu management, inventory and labor management, and team member tools.”  We will look for information no later than next year on how well that is working out.

The other told us, about a patient, that “A.I. Saved His Life by Discovering New Uses for Old Drugs” (Kate Morgan, The New York Times, March 20^th).  The wife of a man “battling a rare blood disorder” and looking at near-certain death contacted a doctor they had met, who used an AI model to propose “an unconventional combination of chemotherapy, immunotherapy and steroids previously untested.”  The patient was “responding to treatment” within a week, and within months was healthy enough for the “stem cell transplant” he needed to put him into remission.  In other cases as well the technology can provide “a systematic way” of assessing “a treasure trove of medicine that could be used for so many other diseases,” which a Harvard Medical School professor called an “enticing alternative” for treating rare afflictions.  Other successes include an AI proposal to use isopropyl alcohol inhalation for nausea which “worked instantly,” ADHD-dedicated amphetamines to help “periodic paralysis in children with a rare genetic disorder,” a drug used for Parkinson’s helping “patients with a neurological condition” to “move and speak,” and more.  This immensely valuable sort of thing, assimilating existing information on side effects, which in these cases were desirable, and other medication properties, more voluminously than humans could do, may be a perfect task for AI.

“How long will the DeepSeek euphoria last?” (Don Weinland, The Economist, March 15^th).  The author said that in that case “many China watchers are now trying to figure out what is real and what is froth,” as “it is worth keeping in mind that China is prone to asset bubbles,” and tension between China and the US could mean that “some of the green shoots might be short-lived.”  We still don’t have the well-established truth about DeepSeek’s asserted ability to produce AI modules at a small fraction of what other companies have paid, and until we do, any comments about its future are premature.

The first tall peak was the subject of “Lila Sciences Uses A.I. to Turbocharge Scientific Discovery” (Steve Lohr, The New York Times, March 10^th).  Per Lohr, “the big, inspiring A.I. opportunity on the horizon, experts agree, lies in accelerating and transforming scientific discovery and development.”  Well, there are others, one of which we will rediscover in the next paragraph.  The Cambridge, Massachusetts startup in the title “had worked in secret for two years to build scientific superintelligence to solve humankind’s greatest challenges.”  It has “already… generated novel antibodies to fight disease and (has) developed new materials for capturing carbon from the atmosphere,” and “turned those experiments into physical results in its lab within months, a process that most likely would take years with conventional research.”  As a result, “many scientists” think “that A.I. will soon make the hypothesis-experiment-test cycle faster than ever before,” and it “could even exceed the human imagination with inventions.”  Although “the early projects are still a long way from market-ready products,” the company “will work with partners to commercialize the ideas emerging from its lab.”  So there is plenty more to do, and while seeing reason for real optimism, comprehensive success is still an unknown amount of time away.

Even more optimistic was New York Times technology columnist Kevin Roose, as he showed in that publication’s “Why I’m Feeling the A.G.I.,” published on March 14^th and in the Sunday, March 16^th print edition.  He wrote “I believe that very soon – probably in 2026 or 2027, but possibly as soon as this year – one or more A.I. companies will claim they’ve created an artificial general intelligence, or A.G.I., which is usually defined as something like “a general-purpose A.I. system that can do almost all cognitive tasks a human can do.””  That means “we are losing our monopoly on human-level intelligence, and transitioning to a world with very powerful A.I. systems in it.”  “Powerful A.I.” will also “generate trillions of dollars in economic value and tilt the balance of political and military power toward the nations to control it.”  Reasons he names for his position are that “the insiders are alarmed,” “the A.I. models keep getting better,” and “overpreparing is better than underpreparing.”  This viewpoint harkens back to two years ago, before most people came to see AI’s doubts, impediments, and shortcomings more clearly.  There have been recent calls for those producing the technology to focus more on specific applications, some as strong as those described in the first two pieces here, and less on the hope of AGI, and this piece does not cancel them out.  Therefore, we have controversy between experts, and the rest of us don’t know either.  When we get much closer, then, I will keep you posted.  In the meantime, this may have been the best all-around month for AI since February 2023.