Look at AI, not ChatGPT

Look at AI, not ChatGPT
 

Background:
 This editorial talks about focusing on AI. It highlights the issues behind launching half-baked products launched by tech giants and how it diminishes a meaningful conversation behind the real uses of AI.
 

What are consequences when half-baked products are launched?
 Quick adoption by Top tech-companies in order to capture the market has come at some costs.
 For example, Google’s own version of ChatGPT called Bard, which made a single mistake in responding to a question about the James Webb Space Telescope sent Google’s parent Alphabet’s shares plummeting, costing the company $100 billion in lost market value.
 Similarly, Microsoft’s Bing had a problematic first outing when it expressed its desire to hack computers and spread misinformation.
 It detracts from more meaningful issues. The media coverage regarding ChatGPT has been myopic and it comes at a cost of insufficient coverage of more societally meaningful uses of AI.
 Short attention and shorter memories is yet another limitation of human intelligence is our attention is ephemeral and we have short memories.
 

What would be an example of a more societally meaningful area of AI? How about AI that affects human
health, where its contributions could be a matter of life and death?
 An AI system, Alphafold, showed it could predict the structure of almost every protein catalogued by science. This could open the door to breakthroughs in the discoveries of medicines and bring efficiencies to processes that cost billions, take decades and deny treatment to so many people.
 The concern is why didn’t Alphafold receive as much attention as Bard, Bing or ChatGPT did? For one, its implications are harder for readers to grasp. Second, it hasn’t delivered immediately usable results.
 Opening the troves of data, providing the appropriate privacy protections and regulatory oversight will be critical to unlocking other AI advances in human health — algorithms for identifying patients at risk of opioid overuse, remotely gauging mental health symptoms or catching signs of breast cancer on mammograms.
 Few of us paid attention to the fact that the first alert of a mysterious new virus out of Wuhan, China, came through AI. Data scraping systems raised a red flag before the humans at the WHO got wind of the impending disaster.
 At the other end, the search for a vaccine was accelerated by algorithms: Researchers got help from AI in understanding the SARS-CoV-2 virus better and predicting how to elicit an immune response.
 

Conclusion
 It is time we paid attention to the right uses of AI and applied more intelligence to how to direct money, talent, data access and regulatory and ethical resources.