This week, AI In Business. The one thing every smart board member asks.
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The last 10 years have brought tremendous growth in artificial intelligence. Consumer internet companies have gathered vast amounts of data, which has been used to train powerful machine learning programs. Machine learning algorithms are widely available for many commercial applications, and some are open source.
Now it’s time to focus on the data that fuels these systems, according to AI pioneer Andrew Ng, SM ’98, the founder of the Google Brain research lab, co-founder of Coursera, and former chief scientist at Baidu.
Ng advocates for “data-centric AI,” which he describes as “the discipline of systematically engineering the data needed to build a successful AI system.”
Article by @VentureBeat
Embedded AI: Rise of the intelligent device
Embedded AI could be a powerhouse – changing the world in ways that aren’t even on the drawing board yet.
Artificial intelligence (AI) is generally viewed in terms of a big computing solution as it makes the leap from the lab to production environments. In the public consciousness, AI is complex algorithms crunching vast amounts of data drawn from hyperscale cloud resources and all of this will create profound, transformative changes to business processes and models.
Lately, however, a different form of AI has emerged: narrower in focus individually and less broad in reach. It’s called embedded AI and because it exists on the device, SoC or even the processor itself it is by nature broadly distributed, particularly out on the edge. This gives it the potential to be an even more significant advancement than enterprise AI, supporting life-changing applications ranging from autonomous vehicles to the metaverse.
Article by @Forbes
Fintech 50 2022: The Newcomers
For our seventh annual Fintech 50, 25 of our picks have never appeared on the list. Crypto companies and startups trying to make banking cheaper and more accessible for small businesses made a particularly strong showing.
Last year, as the pandemic continued to push more buying and banking online and the price of both bitcoin and tech stocks climbed, venture capitalists poured money into fintech— a total of $133 billion globally, almost three times the $49 billion they invested the year before, according to CB Insights.
That flood of capital carried so many promising startups to the next level that our job at Forbes of whittling down the fintech universe to the 50 most innovative startups was the hardest it’s ever been. In fact, there were so many intriguing candidates that half of our picks on this list, our seventh annual Fintech 50, have never appeared on the list before. That’s the highest number of newcomers we’ve had since the list debuted in 2015.
Article by @theedgemalaysia
Half of finance AI projects will be delayed or cancelled by 2024
Gartner said digital automation in finance often fails to meet the expected benefits outlined in business cases for deploying such technologies.
Half of current finance artificial intelligence (AI) deployments will be either delayed or cancelled by 2024, while the use of business process outsourcing (BPO) for AI will rise from 6% to 40% within two years.
In a statement on Tuesday (June 7), technology and consulting firm Gartner Inc said chief financial officers (CFOs) face major barriers to scaling up the use of AI in-house and will increasingly turn to BPO solutions to meet their digital transformation objectives.
Article by @ScienceAlert
Did an AI Really Invent Its Own ‘Secret Language’?
“Secret language” highlights existing concerns about the robustness, security, and interpretability of deep learning systems.
A new generation of artificial intelligence (AI) models can produce “creative” images on-demand based on a text prompt. The likes of Imagen, MidJourney, and DALL-E 2 are beginning to change the way creative content is made with implications for copyright and intellectual property.
While the output of these models is often striking, it’s hard to know exactly how they produce their results. Last week, researchers in the US made the intriguing claim that the DALL-E 2 model might have invented its own secret language to talk about objects.
Article by @Forbes
“Nose in, fingers out.” That’s the rule—of thumb, so to speak—for corporate boards. Board members already have a long list of mission-critical issues in their sights, including risk and reputation management, advising and encouraging the CEO, financial performance, and growth opportunities. Naturally, nobody expects them to get involved in the nitty-gritty of technical operations as well.
The question is: “Why do we need to discuss Artificial Intelligence?”
That is what I usually hear when I encourage board members to put AI on the agenda. But while the question remains the same, the reasons behind board members’ bemusement varies—and that speaks volumes about misconceptions over what is coming down the proverbial pipe.