It combines with domain knowledge from epidemiologists, analyzes over 100,000 reports daily (multiple languages), then sends out regular alerts to health care, government, business, and public health clients. Highlights outbreaks discovered by AI and their risk.
AI has also been adopted to detect people with fever in large crowds via AI-powered smart glasses. They are worn by security guards who can now check hundreds of people within a few minutes without making contact.
A variation on this tech was/is used in bus and train stations too. They combine AI with new temp measurement tech via computer vision. Obviously manual temp measurement increases risk of cross-infection).
Various AI programs are now available for chest screening. These can highlight lung abnormalities in a chest X-ray scans, used to assist COVID-19 risk evaluation (much faster than human radiologists).
AI-based robots are being used to reduce contact between patients and health care workers minimizing cross-infection risk. Chinese firms are using drones to perform contactless delivery and to spray disinfectants in public areas.
Contact-tracing apps are already in widespread use in Asia. They're using AI to determine the risk of cross infection, then alert users of the risk. Canada is also set to release their AI-powered version.
In the attempt to speed things up relative to the usual step-by-step, linear approach to drug discovery, AI is being used to check whether existing drugs used to treat other diseases can be used to treat COVID-19.
Also in remote communication, telemedicine, and food security. Government is using ML enabled chatbots for contactless screening of COVID-19 symptoms. Also to answer questions from the public. Uses real-time info from the French government.
Mantle Labs is offering their AI crop-monitoring solution to retailers free of charge, to provide additional resiliency and certainty to supply chains in the UK due to covid19 impact.
Molecular design/drug discovery. Supply chain. Contactless robots. Vision systems. Some suggest donating data may be the most effect way to fight covid.
Having access to medical data raises privacy issues, making it more difficult for AI to make inroads to healthcare relative to other fields. Also, epidemiologists aren't exactly known for their cutting-edge use of analysis techniques.
And rest assured, the push towards "explainable AI" BS makes government institutions very slow to adopt more appropriate techniques like machine learning that are more inline with the problem complexity.
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Attempts to break this model usually focus around bootstrapping, enabling companies to grow organically, from the bottom-up; albeit much more slowly than their VC-back counterparts.
3/n
Accepting slower growth is supposed to mean making something better, but better how? Are the products produced by smaller firms better?
Important work here; goes well-beyond physics. I hope upcoming generations of scientists pay close attention.
Some of my quick thoughts:
2/12
What is the qualifier for unification under this framework? How will QM and GR demonstrably work together enough to say they are unified"?
3/12
It would be great to have some distance metric between the shapes you create with this approach and known shapes in nature (some connection between the isomorphic classes of nature's shapes and those generated here).
If you over-manage your distractions you’ll miss out on the surprisal needed to fuel creative efforts. But, if everything is unstructured you’ll fail to maintain momentum on your most important tasks.
Nature exhibits pareto-esque distributions for a reason. It needs to tap into variation (surprisal) to make serendipitous discoveries, but those discoveries must benefit some structured goal.
Like anything important in life, it’s best not to think dualistically. Distractions are good to a point. Structure is good to a point. Too much of either will kill you. Dose response.
Companies, particularly large ones, hire "cogs." You fit into an existing recipe that has made the company money for years. Cog work is highly-focused labor that allows companies to scale by division of work.
Many are unwilling to challenge established ideas for fear they lack the knowledge necessary to advance opposition.
But truth accommodates the dissident far more than those hoping to maintain the commonly accepted.
2/5
2 important points to understand:
1) The power of refutation lies in epistemic asymmetry; it requires far less information to refute a theory than to support it.
2) All attempts to assess a model’s validity must remain agnostic to the tools used to build and promote it.
3/5
This means theory exists in a constant state of susceptibility to counter evidence, and that countering does not require familiarity with the language used to defend a model.