While nearly all organizations believe AI would benefit their operations, very few have implemented it. Here are four best practices that can speed your implementation
"Our neighbor is a brew master for a large brewery, and he wants to retire, but the company doesn't want him to leave. The problem is that no one else and nothing else can do his job. It begs the question: why can't artificial intelligence (AI) obtain the necessary recipes and knowledge from him, and then do the job?
Perhaps the answer can be found in an April, 2021, survey of 700 IT professionals and C-level executives conducted by Juniper Networks, The survey showed that while 95% of survey respondents felt that their companies would benefit by embedding AI in their operations, only 6% had actually implemented AI operationally...."
The production of AI systems that power the products we use has undergone a rapid transformation over the past decade. Companies previously poured resources into teams to come up with new algorithms but are now likely to use existing systems to create models that are constantly improving
'Training data is really the new code,' Manu Sharma, the CEO of data training platform Labelbox, said at VentureBeat's Transform 2021 virtual conference on Monday. 'It is essentially what makes AI systems understand what we want the AI to do. 'It's' the medium through which we tell a computer about our real world and how to make decisions.'
What is a data engine?
A data engine is a closed-loop system where a product or service is producing data in a form that can be used to continuously train an AI system, Sharma explained. Models are being trained periodically, and those models are deployed back into applications, generating new kinds of data. This continuous loop makes an AI system better over time..."
Smart AI doesn't happen in a vacuum. It requires smart people with access to computing power.
"Digital giants dominate the cloud and ecommerce markets, and part of the reason for their dominance is artificial intelligence and advanced analytics. The good news is that mainstream enterprises can learn from their experiences and employ cutting-edge technologies.
That's the word from R. "Ray" Wang who provides a roadmap for AI success in his latest book, Everybody Wants to Rule the World. Wang calls the capability needed to move forward 'AI Smart Services" that help automate precision decisions. "To fine-tune precision decisions at scale-that is, to develop decision velocity, [data-driven enterprises] must automate the process of turning signal intelligence into a decision or action. And the way to do this is by creating AI smart services-automated processes powered by AI' ..."
Manual risk management is a thing of the past; AI in risk management is here to stay. Uncover the benefits, use cases and challenges your organization needs to know about
"There are many potential benefits of machine learning and AI for risk management and security-oriented use cases. Many AI risk management offerings rely on the mass computing scale achievable in the cloud, where large quantities of unstructured data can be analyzed and processed rapidly.
Risk management analytics that use cloud-based AI can help organizations evaluate the following:
- uncertain conditions or situations;
- the likelihood of a condition or situation occurring based on context; and
- the effects the occurrence may have, i.e., the possible outcomes.
Risk management tools that use AI can often be integrated into security automation workflows. Additionally, they can also help security leaders make decisions during incidents, business continuity planning, fraud investigations and more..."
Despite the hype, especially around self-driving cars, AI is writing code, designing Google chip floor plans, and telling us how much to trust it.
"Given just how much of the AI hype is just that-hype-it's easy to forget that a wide range of companies are having real success with AI. No, I'm not talking about Tesla's continued errant marketing of AI-infused 'full self-driving.' As analyst Benedict Evans writes, ''V'ersion nine of 'Full Self-Driving' is shipping soon (in beta) and yet will not in fact be full self-driving, or anything close to it.' Rather, I'm talking about the kinds of real-world examples listed by Mike Loukides, some of which involve not-so-full self driving.
To make AI work, you're going to need money and good data, among other things, a recent survey suggests. Assuming these are in place, let's look at a few areas where AI is making headway in making our lives better and not merely our marketing..."
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