AI News, Mark Cuban on backing AI start artificial intelligence
Tap AI-Powered Medical Device Stocks as FDA Bolsters SaMD
The global financial chaos over the past several months has made us overlook many interesting facts, one of which is definitely the FDA’s proposed regulatory framework to try and prioritize the rise of AI-based medical devices among others.
This apart, there is the associated methodology used for implementation of those changes in a controlled manner, thereby enabling risk management for patients, which is also defined as the “Algorithm Change Protocol.” According to the FDA, thisproposed regulatory framework will enable it and the manufacturers to assess and monitor a software product from its premarket development to postmarket performance.
“This potential framework allows for the FDA’s regulatory oversight to embrace the iterative improvement power of artificial intelligence and machine learning-based software as a medical device (SaMD), while assuring patient safety,” FDA states.
In this context, non-healthcare bigwigs like Apple AAPL, Google, Amazon AMZN, International Business Machines Corporation IBM and Microsoft deserve special mention as these leading companies with their digital affluence are already eyeing the AI part of the healthcare and device market of late, courtesy of its bountiful prospects. The latest FDA action on SaMD certainly hallmarks the fact that it is high time that investors should focus more on the AI-driven Medical Device stocks with high potential for tremendous growth down the line.
In 2016, the company partnered with IBM’s Watson health unit to utilize the latter’s machine learning algorithms for incorporate AI in its diabetes app, MiniMed. In 2017, both jointly launched the first artificial pancreas systems.
In 2018, the company acquired Nutrino, an AI powered personalized nutrition platform, in order to boost Medtronic’s offerings for diabetic people and to deliver the company’s predictive glycemic response algorithm.
The National Basketball Association (NBA) led investments in a basketball analytics app backed by Toronto Raptors point guard Jeremy Lin as part of the league's efforts to embrace advanced technology.
The NBA's adoption of HomeCourt represents another win for AI in sports, following the technology's expanded presence in financial services, manufacturing, public security and other sectors.
AI in sports has largely been adopted in so-called wearable gadgets, which help analyse the movement of athletes in a particular sport and monitor certain health markers during intense training.
All that data is designed to help a player measure the accuracy of shots taken from different areas of the court, as well as a player's running speed and height of vertical jump.
They also plan to add interactive training drills and other challenges jointly with various NBA teams and players during the league's regular season, which starts in the last week of October.
Lin, who uses HomeCourt to help guide his workouts, said the app's value goes beyond elite athletes because it was designed to make basketball training more accessible to ordinary players without professional coaching.
"Especially in certain places where people love the game but there isn't a huge abundance of coaches who can help, this is where the app can be so powerful because it can really influence the next generation of players just by providing the right curriculum,"
The HomeCourt app marries a passion for technology with old-school deliberate practice and skill developmentAdam Silver, NBA Commissioner He said that initial idea turned into a more sophisticated concept about tracking and helping improve the performance of basketball players.
Node inks $16 mln
Node’s new platform uses cutting edge AI to enable businesses to rapidly deploy and scale product features that allow their customers to drive transformational outcomes across sales, marketing, recruiting, investing, and more.
“These are all examples of the dynamic predictions Node can power in third party applications.” Node has been quietly working with well known category creating companies including Information Builders (Business Intelligence), Yesware (Sales Engagement), Nimble CRM (Contact Management), ConnectAndSell (Sales Acceleration), Aventri (B2B Events Management) who are leveraging Node to invent the future of their respective markets (see customer testimonials below boilerplate).
Having spent over three decades connecting people with opportunity at massive scale at the leading independent ad-tech exchange OpenX and leading human capital management platform Taleo both before and after it was acquired for $1.9 billion by Oracle, Radovancevich is leading all product development across engineering and product management at Node.
“The Node platform enables a new era of game-changing AI-first applications that solve the most challenging complex business and consumer problems, connecting people with opportunity faster and at scale.” Node is also announcing $16 million in a new financing round that includes funding from existing investors such as Mark Cuban, Avalon Ventures, NEA, NewView Capital, GingerBread Capital, Canaan Partners, and new investors including JetBlue Technology Ventures, Will Smith’s Dreamers Fund, MS&AD, Wharton Alumni Fund, Plum Alley, and former Google and Microsoft executive James Whittaker.
Understanding Job Loss Predictions From Artificial Intelligence
Executive Summary The Variety of AI Job Loss Predictions Worries about artificial intelligence (AI) tend to emanate from concerns about the impact of the new technology on work.
Mark Cuban, for example, warned of the impending doom: “Literally, who you work for, how you work, the type of work you do is going to be completely different than your parents within the next 10 to 15 years.” Kai-Fu Lee, the founder of venture capital firm Sinovation Ventures, has claimed multiple times that robots are likely to take some 50 percent of jobs in the next decade.
First, machine learning (ML) researchers classified occupations as being automatable or not automatable, and occupations were given either a 1 or a 0 if “the tasks of this job [could] be sufficiently specified, conditional on the availability of big data, to be performed by state of the art computer-controlled equipment.” The rest of the paper relied upon “the occupations about which we were most confident” whether they would be either automated or not, which totaled 70 jobs.
They write, it is “largely already technologically possible to automate almost any task, provided that sufficient amounts of data are gathered for pattern recognition.” Assuming that every job is automatable, the researchers identified nine broad variables which could be routinized from O*NET data, which has detailed descriptions of the skills needed for various jobs across the world.
For example, the ML researchers thought that both surveyors and judicial law clerks would become automated, but the model predicted both were in the medium-risk category at 38 percent and 41 percent.
As the report explains, the 70 selected occupations were those “whose computerisation label we are highly confident about, [which] further reduces the risk of subjective bias affecting our analysis.” But selecting those occupations which everyone agrees upon doesn’t reduce bias;
Since it contains data on the breakdown of tasks by job for countless occupations, the researchers were able to replicate the Oxford Study’s techniques to find that just 9 percent of U.S. jobs would be lost due of automation.
McKinsey then surveyed all of the jobs currently available and broke down those jobs into “performance capabilities needed for each activity based on the way humans currently perform them.” It further broke down activity into 18 capabilities and assessed their automation potential.
From here it assumed that “each hour of work that could be automated will result in proportional job loss, for example if 10 percent of current work activity hours in an occupation will be automated, then 10 percent of jobs in that occupation will be displaced.” McKinsey organized its projections into early-, mid-, and late-adoption scenarios, and provided a range of how many jobs worldwide that will face automation.
In the case of nursing homes, the implementation of automation technologies decreased the staffing levels by 5.8 percent in high-end nursing homes, while low-end homes saw an increase in staffing by 7.6 percent.
As the authors of the study pointed out, “these findings suggest that the impact of automation technology on staffing decisions depends crucially on a facility’s strategic position in the local marketplace.” A study of Spanish manufacturing firms found that more productive firms are more likely to adopt robots, which leads to substantial output gains.
At the same time, the report found “substantial job losses in firms that do not adopt robots, and a productivity-enhancing reallocation of labor across firms, away from non-adopters, and toward adopters.” Research into one specific Dutch company undergoing automation found similarly complex impacts.
These lost wage earnings were only partially offset by various benefits systems, but the lost earnings were disproportionately borne by older workers and workers with longer firm tenure.
Pascual Restrepo made clear, there should be “no presumption that adjustment to the changed labor market brought about by rapid automation will be a seamless, costless and rapid process.” It takes time for companies to adopt new technologies, incorporate them into decisions processes, and bring them to market.
This reality was clearly true of the development of electricity, which took decades to diffuse because it was generally unwise to immediately replace manufacturing plants with a new and expensive technology that didn’t yield a huge return.
Fourth, most predictions assume, as the Oxford Report does, that it is “largely already technologically possible to automate almost any task, provided that sufficient amounts of data are gathered for pattern recognition.” Yet, the jury is out on this very bold assumption.
Judea Pearl of the UCLA Computer Science Department, who is highly regarded for his research in this area, recently commented that, “All the impressive achievements of deep learning amount to just curve fitting.” Because of the difficulty in developing autonomous systems, many startups have simply hired humans to look like AI.
- On Monday, December 9, 2019
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