Training AI/ML algorithms
A new survey commissioned by Alegion reveals that 8 out of 10 companies have found training AI/ML algorithms more challenging than originally anticipated. Moreover, 78 percent of AI/ML projects have stalled at some stage before deployment. 96 percent of enterprises have also encountered data quality and labeling challenges – while only half of enterprises have released an AI/ML project into production.
In addition, only 65 percent of projects have reached the data labeling and algorithm training phase, while 72 percent of those surveyed report that production-level model confidence will require more than 100,000 labeled data items. Nevertheless, 74 percent are using AI and ML projects to disrupt the marketplace, while 82 percent are satisfied with the progress of their respective projects.
“The single largest obstacle to implementing machine learning models into production is the volume and quality of the training data,” states Nathaniel Gates, CEO and co-founder of Alegion. “This research reinforces our own experience, that data science teams new to building ROI-driven systems try to tackle training data preparation in house and get overwhelmed.”
Gates also notes large businesses with more than 100,000 employees are most likely to have an AI strategy – but only 50 percent of them currently have one. Alegion’s survey, says Gates, confirms previous research that AI is still nascent in the enterprise.
AI spending to grow nearly $35.8 billion in 2019
Despite the evolutionary growing pains described above, IDC forecasts that worldwide spending on AI systemswill reach $35.8 billion in 2019, representing an increase of 44 percent over the amount spent in 2018. This number is expected to more than double to $79.2 billion in 2022 with a compound annual growth rate (CAGR) of 38 percent.
More specifically, IDC analysts projects that global spending on AI systems will be led by the retail industry, with companies investing $5.9 billion in 2019 on items such as automated customer service agents, as well as expert shopping advisors and product recommendations. Meanwhile, the banking sector is expected to allocate $5.6 billion for AI-enabled solutions such as automated threat intelligence and prevention systems, as well as fraud analysis and investigation systems. Discrete manufacturing, healthcare providers and process manufacturing will complete the top 5 industries for AI systems spending in 2019.
AI/ML to take center stage at Hot Chips
Perhaps not surprisingly, AI/ML will be taking center stage at Hot Chips 2019, with Rick Merritt of the EE Timesreportingthat half of the talks at the 2019 summer conference are slated to focus on AI acceleration. The annual gathering for microprocessor designers, says Merrit, traditionally focused most of its talks on CPUs for PCs and servers.
“If Rip Van Winkle fell asleep in 1999 and woke up now, he would be astounded by all the attention to machine learning and AI, which were pretty much just research topics when he started his nap,” veteran microprocessor analyst Nathan Brookwood of Insight64 tells the EE Times. “[But Rip would] be pretty comfortable with about half of the papers on this year’s Hot Chips agenda because they are fairly straightforward extrapolations of past conferences. Intel, AMD and IBM are still slugging it out to get more performance out of architectures [that] Rip already knew.”