AI News, Artificial Intelligence: 3 charts reveal what hospitals need in the near ... artificial intelligence
How artificial intelligence can deliver real value to companies
Companies new to the space can learn a great deal from early adopters who have invested billions into AI and are now beginning to reap a range of benefits.
In a McKinsey Global Institute discussion paper, Artificial intelligence: The next digital frontier?, which includes a survey of more than 3,000 AI-aware companies around the world, we find early AI adopters tend to be closer to the digital frontier, are among the larger firms within sectors, deploy AI across the technology groups, use AI in the most core part of the value chain, adopt AI to increase revenue as well as reduce costs, and have the full support of the executive leadership.
In our survey, early AI adopters that combine strong digital capability with proactive strategies have higher profit margins and expect the performance gap with other firms to widen in the next three years.
Governments also must get ahead of this change, by adopting regulations to encourage fairness without inhibiting innovation and proactively identifying the jobs that are most likely to be automated and ensuring that retraining programs are available to people whose livelihoods are at risk from AI-powered automation.
Both countries have grown AI “ecosystems”—clusters of entrepreneurs, financiers, and AI users—and have issued national strategic plans in the past 18 months with significant AI dimensions, in some cases backed up by billions of dollars of AI-funding initiatives.
This Chart Reveals Where AI Will Impact Recruiting (and What Skills Make Recruiters Irreplaceable)
LinkedIn’s CEO Jeff Weiner recently predicted that the most important trends impacting the future of work are - AI and automation, the skills gap, and the rise of independent work.
So jobs that involve collecting data (read: resumes), processing data (such as parsing/de-duping/matching profiles) and other predictable activities that deliver based on well-defined rules (e.g., social media aggregation, ATS updating) are the ones on top of the list for automation.
3 charts that show the unstoppable growth of Legal Tech
The In-House Counsel’s Legal Tech Buyer’s Guide 2018 launched this week reflects a period of massive disruption in the $600 billion global legal services market.
Here are three charts that show the unstoppable growth of legal technology taken from the 80+ page free analysis of 130+ legal tech players transforming the profession.
There has been an increase of 65% in legal tech companies utilizing AI compared to our corresponding study last year (there are now 67 legal AI players, according to the above chart).
The current applications of AI in daily legal work includes everything from drafting and reviewing contracts, mining documents in discovery and due diligence, answering routine questions or sifting data to predict outcomes.
Legal tech in 2017 saw $233 million in investments in companies across 61 deals, edging ahead of 2016 with $224 million in investments across 79 deals.
Reshaping Business With Artificial Intelligence
Perhaps the most telling difference among the four maturity clusters is in their understanding of the critical interdependence between data and AI algorithms.
Compared to Passives, Pioneers are 12 times more likely to understand the process for training algorithms, 10 times more likely to understand the development costs of AI-based products and services, and 8 times more likely to understand the data that’s needed for training AI algorithms.
Most organizations represented in the survey have little understanding of the need to train AI algorithms on their data so they can recognize the sort of problem patterns that Airbus’s AI application revealed.
Citrine Informatics, a materials-aware AI platform helping to accelerate product development, uses data from both published experiments (which are biased toward successful experiments) and unpublished experiments (which include failed experiments) through a large network of relationships with research institutions.
knowing what does not work helps it to know where to explore next.3 Sophisticated algorithms can sometimes overcome limited data if its quality is high, but bad data is simply paralyzing.
Other data is fragmented across data sources, requiring consolidation and agreements with multiple other organizations in order to get more complete information for training AI systems.
The need to train AI algorithms with appropriate data has wide-ranging implications for the traditional make-versus-buy decision that companies typically face with new technology investments.
Training AI algorithms involves a variety of skills, including understanding how to build algorithms, how to collect and integrate the relevant data for training purposes, and how to supervise the training of the algorithm.
The chief information officer of a large pharma company describes the products and services that AI vendors provide as “very young children.” The AI tech suppliers “require us to give them tons of information to allow them to learn,” he says, reflecting his frustration.
We believe the juice is not worth the squeeze.” To be sure, for some support functions, such as IT management and payroll support, companies might choose to outsource the entire process (and pass along all of their data).
Even if companies expect to rely largely on external support, they need their own people who know how to structure the problem, handle the data, and stay aware of evolving opportunities.
“Five years ago, we would have leveraged labor arbitrage arrangements with large outsourcers to access lower cost human labor to do that work,” the pharma company CIO says.
Eric Horvitz, director of Microsoft Research, argues that the tech sector is quickly catching up with the new model of offering technology tools to use with proprietary data, or “providing industry with toolsets, computation, and storage that helps to democratize AI.” Many AI algorithms and tools are already in the public domain, including Google’s TensorFlow, GitHub, and application programming interfaces from tech vendors.
The data issues can be pronounced in heavily regulated industries such as insurance, which is shifting from a historic model based on risk pooling toward an approach that incorporates elements that predict specific risks.
But we use machine learning algorithms to assess the model’s non-linear construction, variables and features entered, and as a benchmark for how well the traditional model performs.” As technology races ahead of consumer expectations and preferences, companies and the public sector tread an increasingly thin line between their AI initiatives, privacy protections, and customer service.
Likewise, a technology vendor offers an AI-based service to help call center operators recognize when customers are getting frustrated, using real-time sentiment analysis of voice data.
- On Friday, January 18, 2019
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