“AI” may be a hot buzzword – and the global market is expected to grow to about $ 310 billion by 2026 – but what does artificial intelligence mean?
The definition of AI can be confusing to pin down because its application is so wide and in the degree of complexity, scope, algorithmic underpinnings and methods used.
For this reason, there is a growing demand for a more advanced, precise definition of AI to “simulate human intelligence processes by machines”. Some consider AI next step – and ultimate evolution – to be cognitive computing.
“It’s a huge, amorphous term that has just come to mean AI,” said Stephen D’Angelis, founder and CEO of Enterra Solutions. The cognitive computing company has made a name for itself in this advanced AI use case, Autonomous Decision Science (ADS), which it says is “ahead of data science.”
AI doesn’t just have to be autonomous, but it has to have the ability to “realize, think, act and learn itself,” Dengelis said. It not only works on its own and the data and insights it receives – but it also makes decisions based on information, as humans do.
Cognitive computing provides human-like reasoning
AI, in its most basic definition, is a simulated process of human intelligence by machines. Machine learning (ML) is its self-learning subset.
Cognitive computing, according to practitioners, exceeds both by taking advantage of techniques such as pattern recognition, natural language and “human perceptions” processing, data mining and other systems trying to mimic the human thought process.
According to Markets and Markets, these types of processes allow cognitive computing systems to work in research to analyze emerging patterns, find business opportunities, and handle complex process-focused issues – all in real time. This can ultimately enhance the interaction by providing “relevant, relevant and valuable information” that can report customized recommendations and decision making, reduce business costs and streamline business processes.
The firm predicts that the size of the global cognitive computing market will grow to $ 77.5 billion by 2025, representing a CAGR of more than 30% by 2020. A number of factors are accelerating this growth, including the continuous evolution of the computing environment (cloud, mobile, analytics). , Increased hybrid deployment models and increased human / machine interaction. The demand for intelligent business processes is also increasing and companies are increasingly using deep learning techniques and using cognitive capabilities to reduce additional operational costs.
A growing number of companies offering cognitive computing tools include Sparkcognition, Neumenta, Deepmind, CognitiveScale and Enterra. Microsoft Cognitive Services, HPE Heaven On Demand and IBM Watson also maintain a strong presence in space.
Enterra describes its ADS platform as a human-like logic AI software that “serves as a data scientist, subject matter expert and trusted counselor.”
Technology combines capabilities such as inference logic, semantic reasoning, symbolic reasoning capabilities, ontology-based rules engine, industry-specific knowledge bases, and general knowledge bases. The glass box is then paired with ML techniques and non-linear optimization functions. The latter two together provide a more transparent, X-ray view of the ML process (as opposed to standard “black box” ML models) allowing the system to solve optimization problems when obstacles or objective functions are nonlinear.
Dengelis explained that using all of this, the system can analyze various data sources on “market momentum”. The system can generate business and consumer insights and understand business processes with precise and limited human intervention. ADS can then make decisions automatically and learn from those decisions. It also explains in simple language what decisions were made and why and how to proceed.
This could help companies optimize revenue drivers, increase supply and demand planning, and gain competitive intelligence, among other benefits, he said.
Dengelis reiterated the fact that the term AI can be overused and misused and is often attributed to technology that is more technically ML. But as he argues, there is nothing “artificial or intelligent” about ML.
ADS and cognitive computing, by contrast, “allow us to gain enterprise scalability for the first time in data analysis,” he said. “We can analyze large areas of the world, most of the businesses.”
Cases of commercial use
Antera focuses its ADS technology largely on global consumer products, Dengelis explained.
An example is the use of case products to promote or give a trade promotion. If the system understands all the constraints of the manufacturer and retailer, it can generate trade promotions using the “control knobs” that are possible for both players who are just looking for the right optimization.
In the Frozen Pizza category, for example, it might look at weekly promotional campaigns and sales, considering when a particular manufacturer had a shutdown and could not make papyrus. It could tinker and “re-optimize at market speeds,” Dengelis said. “AI can optimize any combination.”
The platform is capable of minimizing processes that took just minutes from a week, he claimed, adding that over the past few years, Covid, Covid-plus inflation, Covid-plus-inflation-plus other waves of inflation have been helping to gain customers. Brought to Ukraine by war.
“You can never predict every problem, but if you can get good feedback, you can reduce the risk, take advantage of market changes,” Dengelis said. “It gives companies a different set of control knobs that they can modulate to affect the goal. It is decoding the dimension of human experience. “
Enterra claims that its ADS is 90% accurate like that of human experts, and its applications have achieved 1,000% annual ROI. The company’s equipment is used by top brands including Nestle, McCormick, Mars, P&G, TPG and Unilever. Dengelis said Entera is experiencing a CAGR of about 95% and is moving towards an IPO in the next 24 months. He reiterated the fact that the future of AI is not ML, but technologies like cognitive computing and ADS. “This is the next wave of analytical innovation,” he said.
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