Artificial Intelligence (AI) is one of the most powerful technologies ever developed, but it's not nearly as new as you might think. In fact, it's undergone several evolutions since its inception in the 1950s.
Mark Gazit writes in TheNetWeb
"The first generation of AI was 'descriptive analytics,' which answers the question, 'What happened?' The second, 'diagnostic analytics,' addresses, 'Why did it happen?' The third and current generation is 'predictive analytics,' which answers the question, 'Based on what has already happened, what could happen in the future?'
While predictive analytics can be very helpful and save time for data scientists, it is still fully dependent on historic data. Data scientists are therefore left helpless when faced with new, unknown scenarios. In order to have true 'artificial intelligence,' we need machines that can 'think' on their own, especially when faced with an unfamiliar situation. We need AI that can not just analyze the data it is shown, but express a 'gut feeling' when something doesn't add up. In short, we need AI that can mimic human intuition. Thankfully, we have it..."
Studies show that your business can experience 40% productivity improvement by using Artificial Intelligence and Machine Learning. It can help you to reorganize your data in such a way that you get value out of every data point that you record.
"Machine Learning is an invaluable technology that more than 50% of businesses are already exploring or planning to adopt," writes Shardul Bhatt in AWS News
"It is a key player in the digital transformation of your organization.
However, while implementing Machine Learning, your business is likely to look at the positive side of things. There are multiple Machine Learning challenges that you may forget even exist.
Solving these Machine Learning problems is crucial to the success of your entire digital transformation initiative. You don't want to get stuck in management struggles or half-hearted Machine Learning projects that yield no result.
In this article, we will highlight the 7 Machine Learning challenges that your business can face while implementing. You will also learn how to find quick solutions to these problems in Machine Learning projects..."
The federal government is increasing its investment in AI research, with the announcement on August 26 of over $1 billion of awards to establish 12 new AI and quantum information science (QIS) research institutes nationwide
"The announcement was from the White House Office of Science and Technology Policy, the National Science Foundation (NSF) and the US Department of Energy (DOE), in a release issued from the Brookhaven National Laboratory of Upton, N.Y.
The $1 billion will go toward NSF-led AI Research Institutes and DOE QIS Research Centers of five years, establishing 12 multi-disciplinary and multi-institutional national hubs for research and workforce development. The goals are to spur innovation, support regional economic growth and advance American leadership in strategic industries..." - aitrends
Google LLC announced today a raft of artificial intelligence-related updates as part of Google Cloud Next: OnAir, a nine-week series of livestream events that runs through Sept. 15.
"The focus of today's updates is all about machine learning," writes
Mike Wheatley in siliconANGLE
, "and, in particular, the emerging MLOps discipline that's aimed at putting machine learning workflows into operation by fostering more collaboration and better communication between data scientists and developers.
Google's AI Platform is a suite of tools that's meant to enable MLOps. It enables machine learning developers, data scientists and data engineers to take their ideas around ML and develop these into actual projects that can be deployed in production quickly and without excessive costs..."
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