AI News, How to get a job working with artificial intelligence/machine learning artificial intelligence
2019 Preview Includes Artificial Intelligence, Machine Learning
The outside innovator would implement a tool inside a real family medicine practice, and the AAFP would monitor progress and then conduct a post assessment.
There are some gaps that the market is not really interested in filling, so the AAFP is creating a challenge program -- akin to an XPrize program(www.xprize.org) -- to explain a gap we want to be filled.
As the physician talks to the patient about what's going on -- the diagnosis, the care -- the watch takes that recording, transcribes it, and uses artificial intelligence to create the initial note.
We're joining a new effort, which is a combination of the Healthcare Services Platform Consortium(www.hspconsortium.org) and the Clinical Information Interoperability Council.(www.hl7.org) The goal is to establish standard models of clinical data so that services and apps could be developed that wouldn't have to be recreated for each individual EHR (electronic health record) or health care organization.
We combined work with those two organizations so that we have a physician-led effort to define the clinical data we need to take care of patients -- data that would be nationally recognized and could be deployed across all different EHRs and other IT products so they can start to interoperate.
So as a single organization of a single specialty, it's hard to imagine that we could make a big impact alone, but I think we can with strong, well-aligned partnerships.
First, I want to see the AAFP deploy some innovative solutions in typical family physician practices and see the positive benefits that result in those practices -- for instance, outcomes that really decrease administrative burden.
think the biggest challenge facing our specialty 10 to 15 years down the road will be in keeping AI and machine learning on the right track -- assisting physicians in our work and not replacing or diminishing the patient-physician relationship.
Artificial intelligence is mastering a wider variety of jobs than ever before
In April, the U.S. Food and Drug Administration permitted marketing of the first artificial intelligence that diagnoses health problems at primary care clinics without specialist supervision (SN: 3/31/18, p.
When playing crater “I Spy” with a different third of the lunar landscape, the AI found 92 percent of previously discovered craters and spotted about 6,000 pockmarks that humans had missed.
One AI that raised eyebrows in 2018 generates realistic fake video footage by making the subject of one video mirror the motions and expressions of someone else in a different clip (SN: 9/15/18, p.
Enter a master naturalist artificial intelligence that learned to identify wildlife by studying 1.4 million hand-labeled images collected by the Snapshot Serengeti citizen science project.
An AI programmed with virtual versions of specialized brain cells called grid cells found shortcuts through a virtual maze better than an AI without the mapping cells (SN: 6/9/18, p.
This secure setup using a novel cryptography system may encourage drug companies to pool their resources, creating larger libraries of training data to beget smarter AI.
But now, an artificial intelligence analyzes both audio and visual cues, like lip movements, to pick out what individual speakers are saying in noisy videos (SN: 7/7/18, p.
Machine Learning And Artificial Intelligence In Business: Year In Review, 2018
and a host of startups announcing new chips focused on both training and run time inference, to announcements of a wide variety of new algorithms, much of the news has been focused on research and academia.
and Amazon, early movers due to their cloud foundation who are working to figure out how to generalize techniques developed for their products, in order to attract the wider business market.
Yes, there are plenty of other companies also working to add machine learning to their product lines, and an even larger body of startups focusing on building solutions with AI and ML techniques at the core, but those four companies are well known and display the two key methods to attack the problem.
The ability for business line management and employees to type or speak “what’s the sales last quarter for region X?” rather than have to deal with drop down list boxes and other more technical UX features is helping move BI from shelf-ware to regular use.
Unlike natural language, ML isn’t as visible to the end user, it is a core technology used to analyze information to help in understanding existing patterns and plan for future actions.
While ML companies who market to specific niches have a chance to grow independently, the odds are that the ones who make the most inroads will be acquired by larger firms who realize that buying can be a more rapid entrance than building.
It wasn’t until companies created core tools that helped systems analysts and business users access BI technology to create their own analysis that we saw the growth of the last decade.
Until the ML industry moves past the reliance on very technical frameworks, ML adoption will primarily come from two areas: As with other new technologies, the challenge of finding enough people to do the work means that the educational sector is getting involved.
What it means is that ML is still early in the adoption cycle, it won’t be approaching the mass market until more companies can incorporate ML techniques into their solutions using a combination of a larger pool of trained employees and higher level tools.
TechEd was one of a number of places showcasing early forms of robotic coordination, leveraging vision, machine learning, and other techniques to help connected robots improve performance in manufacturing, shipping and other business functions.
Rather than business analysts attempting to extract what people claim to be doing and then defining automated processes, the ML systems observe what people are regularly doing and then the systems can automate the tasks.
The key to the RPA message is that the systems aren’t replacing people, they’re only automating the most routine tasks, allowing the skilled workers to focus on exceptions and more complex work.
More importantly, what is society doing in order to ensure that displaced workers can either be trained for a new economy or are taken care of in a way that a modern, civilized, society should be capable of doing.
The issue is that the move from manufacturing buggy whips to manufacturing car parts was not as big a jump as going from a manufacturing floor to working in a machine learning framework.