AI News, Artificial Intelligence and the coming of the self artificial intelligence
Artificial intelligence is coming to medicine — don’t be afraid
A McKinsey Global Institute report suggests that AI is helping us approach an unparalleled expansion in productivity that will yield five times the increase introduced by the steam engine and about 1 1/2 times the improvements we’ve seen from robotics and computers combined.
Requirements include empathy, information management, application of expertise in a given context, negotiation with multiple stakeholders, and unpredictable physical response (think of surgery), often with a life on the line.
The reason this feat was so impressive is due to the high branching factor and complexity of the Go game tree — there are an estimated 250 choices per move, permitting estimates of 10 to the 170th different game outcomes.
Since doctors are increasingly overburdened with clerical tasks like electronic health record entry, prior authorizations, and claims management, they have less time to practice medicine, do research, master new technology, and improve their skills.
Physicians would rather practice at the top of their licensing and address complex patient interaction than waste time entering data, faxing (yes, faxing!) service authorizations, or tapping away behind a computer.
It’s the equivalent of asking an airline pilot to manage the ticket counter, count the passengers, handle the standby and upgrade lists, and give the safety demonstrations — then fly the plane.
Perhaps it makes sense to start with automated interpretation of basic labs, dose adjustment for given medications, speech-to-text tools that simplify transcription or document face-to-face interactions, or even automate wound closure.
This hybrid model of humans and machines working together presents a scalable automation paradigm for medicine, one that creates new tasks and roles for essential medical and technology professionals, increasing the capabilities of the entire field as we move forward.
Decentralized Artificial Intelligence Is Coming: Here's What You Need To Know
The existing AI market is increasingly controlled by tech giants like Google, IBM and Microsoft, all of which offer cloud-based AI solutions and APIs.
This model assumes little control of users over the AI products, and in the long run, such a centralized model could lead to the monopolization of the AI market.This could cause unfair pricing, a lack of transparency, interoperability and limited participation of smaller companies in AI innovation.
Companies can also combine the expertise of different cybersecurity AI agents on the network, which will safely exchange security information, outsource tasks and cooperate in solving common security issues.
You need an autonomous AI solution that runs in the decentralized environment and implements contractual obligations:By definition, centralized proprietary solutions cannot be exposed to many users in the decentralized network.
You need an AI optimized for the on-device performance and not dependent on network connectivity:Due to network connectivity problems, battery power constraints and low computing power, mobile devices are not a good option for running cloud-based AI software.
At the same time, centralized AI still remains a good option if you need a very generalized ML model that you can easily plug into your application.Google, Microsoft and IBM have developed the best generalized machine learning models on the market that are trained on huge data sets and built according to the top ML standards and bleeding-edge ML algorithms.
In the long run, decentralized solutions can produce the radical democratization of the AI market, optimization of solutions for a wide variety of use cases, easy integration and communication between different algorithms through a single protocol and the development of interoperability standards, which will ultimately lead us to the era of AGI (artificial general intelligence).
Another AI winter could usher in a dark period for artificial intelligence
But AI only moved from the mythical realm to the real world in the last half-century, beginning with legendary computer scientist Alan Turing’s foundational 1950 essay asked and provided a framework for answering the provocative question, “Can machines think?” At that time, the United States was in the midst of the Cold War.
In 1969, Congress mandated that the Defense Advanced Research Projects Agency, or DARPA, fund only research with a direct bearing on military efforts, putting the kibosh on numerous exploratory and basic scientific projects, including AI research, which had previously been funded by DARPA.
(“Informatics” and “machine learning,” the paper notes, were among the euphemisms that emerged in this era.) The late 1970s saw a mild resurgence of artificial intelligence with the fleeting success of the Lisp machine, an efficient, specialized, and expensive workstation that many thought was the future of AI hardware.
To use a now-classic example, you can feed a neural net thousands of images, some labeled “cat” others labeled “no cat,” and train the machine to identify “cats” and “no cats” in pictures on its own.
- On 5. marts 2021
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