The Big Tech crash that started in 2022 has now extended into 2023. The Big Tech companies have continued with massive layoffs here in the first few weeks of 2023. Here are some recent headlines: "Microsoft to Layoff 10,000 Employees as Part of a Massive Cost-Cutting Initiative" - "Amazon Announces Massive, Unprecedented Layoffs, Citing 'Uncertain Economy'" - "Google parent company Alphabet to eliminate 12,000 jobs" - "Facebook Parent Meta Is Preparing to Notify Employees of Large-Scale Layoffs This Week" - One of the big news stories of 2022 was the virtual death of the concept of fully autonomous self-driving vehicles. After decades of spending $billions of revenue in trying to develop a fully autonomous self-driving vehicle, Ford, Volkswagen, and many others finally pulled the plug on continuing to dump money into this concept, realizing that they had wasted significant resources in trusting the Technocrats and their techno-prophecies. The biggest B.S. AI story of the week, and one that was reported in multiple Alternative Media sources, was about Nita Farahany's presentation at the World Economic Forum regarding AI "decoding brain activity." And judging by how many publishers in the Alternative Media ran with this story, it is obvious that many actually believe that this science fiction is real.
Late last year, the FDA released a draft guidance document that will loosen the reigns on some types of medical software. This development is part of a larger trend in which artificial intelligence (AI) and other technologies take on a more central role in the doctor/patient relationship. It is “evidence-based” medicine, as determined by government regulators. The FDA’s guidance deals with “clinical decision support” software (CDS). These are programs that “analyze data within electronic health records to provide prompts and reminders to assist health care providers in implementing evidence-based clinical guidelines at the point of care.” Make no mistake: CDS is the future of medicine. The AI health market is projected to hit $6.6 billion by 2021; in 2014 it was $600 million. AI supported by machine learning algorithms is already being integrated in the practice of oncology. The most common application of this technology is the recognition of potentially cancerous lesions in radiology images. But we are rapidly moving beyond this to where AI is used to make clinical decisions. When a mistake is made, who is accountable? The algorithm, or the doctor? How do we hold an algorithm accountable? There are other problems when using AI machine learning in medicine. IBM has developed Watson for Oncology, a program that uses patient data and national treatment guidelines to guide cancer management. As we’ve seen, Google and other tech giants are greedy for our health data so they can develop more of these tools. Technology should absolutely be harnessed to improve medicine and clinical outcomes, but AI cannot replace the doctor/patient relationship. There are obvious ethical questions.