AI News, Machine Learning Engineer
- On Sunday, September 16, 2018
- By Read More
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- On Tuesday, September 18, 2018
- By Read More
Artificial intelligence: Thetime to act is now
Artificial intelligence will soon change how we conduct our daily lives.
With better algorithms and increased stores of data, the error rate for computer calculations is now often similar to or better than those of human beings for image recognition and several other cognitive functions.
This technology relies on complex neural networks that process information using various architectures, comprised of layers and nodes, that approximate the functions of neurons in a brain.
First off, value capture will initially be limited in the consumer space, and companies will achieve most value by focusing on enterprise “microverticals”—specific use cases within select industries.
Our analysis of the technology stack also suggests that opportunities will vary by layer and that the most successful companies will pursue end-to-end solutions, often through partnerships or acquisitions.
For certain hardware players, AI might represent a reversal of fortune, after years of waning interest from investors who gravitated toward software, combined with heavy commoditization that depressed margins.
To bring some clarity to the seemingly chaotic supply landscape, we divided the machine-learning (ML) and DL technology stack into nine layers, across services, training, platform, interface, and hardware (Exhibit 1).
Such product enhancements definitely appeal to consumers—they may, for instance, increase the amount of time someone spends on a web site—but they don’t produce a direct uptick in sales or revenue.
Our early analysis of data from McKinsey Global Institute, combined with expert interviews and research, revealed nearly 600 discrete uses for AI across major industries.
Here are a few AI applications that could see high demand over the next few years because of their strong visual-perception and processing capabilities: Companies face a difficult task when deciding which opportunities to pursue, among the hundreds available, but they can narrow their options through a structured approach.
Also important is the potential for disruption within an industry, which we estimated by looking at the number of AI use cases, start-up equity funding, and the total economic impact of AI, defined as the extent to which solutions reduced costs, increased productivity, or otherwise benefited the bottom line in a retrospective analysis of various applications.
When we considered value at stake in combination with maturity, it became clear that several industries now offer the strongest opportunities for AI: public sector, banking, retail, and automotive (Exhibit 3).
While the public sector’s prominence may seem surprising in an age where governments are cutting budgets, many officials see the value of AI in improving efficiency and efficacy, and they are willing to provide funding.
Buyers aren’t interested in AI just because it’s an exciting new technology—instead, they want AI to generate a solid return on investment (ROI) by solving specific problems, saving them money, or increasing sales.
To win in AI, companies must offer, or orchestrate, end-to-end solutions across all nine layers of the technology stack because many enterprise customers struggle to implement piecemeal solutions.
A hospital, for instance, would prefer to purchase a system that included both an MRI machine and AI software that makes a diagnosis, rather than getting these components separately and then trying to make them work together.
There have been over 250 acquisitions involving private companies with AI expertise since 2012, with 37 of these occurring in the first quarter of 2017.1 1.“The race for AI: Google, Baidu, Intel, Apple in a rush to grab artificial intelligence startups,”
While hardware has become commoditized in many other sectors, this trend won’t reach AI any time soon because hardware optimized to solve each microvertical’s problems will provide higher performance, when total cost of ownership is considered, than commodity hardware, such as general-purpose central processing units (CPUs).
But accelerators optimized for long short-term memory networks are better suited to speech recognition and language translation and thus would appeal to makers of sophisticated virtual home assistants.
As seen with the advent of DL accelerators, hardware alone or in combination with software will likely enable significant performance improvements, such as decreased latency or power consumption.
Within cloud hardware, customers and suppliers vary in their preference for application-specific integrated circuit (ASIC) technology over graphics processing units (GPUs), and the market is likely to remainfragmented.
At the edge, ASICs will win in the consumer space because they provide a more optimized user experience, including lower power consumption and higher processing, for many applications.
Those costs represent about 27 percent of Nvidia’s total revenue—much higher than the peer group average of 15 percent—and they show that Nvidia is willing to take a different path than many semiconductor companies, which are aggressively cutting R&D expenditures.
The company is aggressively training developers on the skills needed to make use of GPUs for DL, funding start-ups that proliferate the use of its GPUs for DL, forming partnerships to create end-to-end solutions that incorporate its products, and increasing the number of GPU-driven applications.
Our investigation suggests the following emerging ideas on the classic questions of business strategy: If companies wait two to three years to establish an AI strategy and place their bets, we believe they are not likely to regain momentum in this rapidly evolving market.
- On Sunday, February 23, 2020
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