It was more than 20 years ago that IBM’s chess-playing computer Deep Blue won a game against Garry Kasparov, the then chess world champion. It was a striking triumph for artificial intelligence (AI) and was a wake-up call to the AI community.
Because chess is a combination of pure calculation and deep strategy borne of experience, Deep Blue’s victory demonstrated that such “hybrid” logical/experiential realms are for the taking in the AI world.
The ancient Chinese strategy game, Go, invented more than 2500 years ago, is well known for its complexity. Despite simple rules, Go is very complex, even more so than chess, and possesses more possibilities than the total number of atoms in the visible universe. According to AI researcher Louis Victor Allis, “Compared to chess, Go has both a larger board with more scope for play and longer games, and, on average, many more alternatives to consider per move.”
In March 2016, the AI program AlphaGo became the first Computer Go program to beat a human professional Go player without handicaps on a full-sized 19×19 board.
It is likely that this is but the latest example of AI establishing dominance in areas combining logic, calculational power, and insights coming from lessons of the past. No matter how many times these feats are duplicated, it is nothing short of amazing that humans can create algorithms that surpass their creators!
The ability to “crunch” data and analyze multi-dimensional information is a clear strength of AI and will certainly lead to revolutionary changes in technology, business, healthcare, and other areas.
Artificial Intelligence and Medical Diagnosis
A Forbes article cites a 2016 study by Frost & Sullivan, which says that the market for AI in healthcare is projected to reach $6.6 billion by 2021, a 40% growth rate. It goes on to say that clinical support from AI will strengthen medical imaging diagnosis processes and using AI solutions for hospital workflows will enhance care delivery. The report also suggests that there is the potential to improve outcomes by 30 to 40 percent at the same time the costs of treatment by as much as 50%.
It is clear that medical diagnosis effectiveness can benefit from AI, through:
- The use of historical data, trends, and statistics
- The ability to analyze a reported set of symptoms or complaints against a database reflecting the information from above
- Conclusions which are not influenced by time or monetary constraints
- Unbiased analysis, “cold” logic, and a lack of pre-conceived notions about patients and demographics
When phrased in the manner of the above list, it is clear that medical diagnosis could easily be dominated by approaches that leverage AI and data analytics capability.
Artificial Intelligence and Insurance Claims Management
An Oliver-Wyman report discusses so-called InsurTechs, such as French company Shift Technology, Claims Control (Lithuania), and Cognotek (Germany), which seek to improve claims process efficiencies and effectiveness.
The report suggests that this is a fertile opportunity because losses and loss adjustment expenses can put a real drag on profits. Small improvements in the claims process can lead to significant benefits in resource usage, expenses, quality assurance, and ultimately, company performance.
In addition, strong data analytics capabilities can help detect and prevent insurance fraud. Many insurance types (e.g., automobile insurance) are susceptible to significant losses from fraud and powerful, cost-efficient fraud detection can reduce expense and lead to larger profits, not to mention better premium rates for the rest of us.
Just-in-Time Supply Chain Management
It is well known that demand, timing, and fulfillment are critical aspects in production and supply chain management. If key supplies or materials are not in place at the right time and in the right location, expenses can increase, production can be delayed or halt, and, above all, profits and reputation can be damaged.
Equally well understood is that having parts or finished products sitting in large warehouses for extended periods is sub-optimal at best, and can even lead to business failure.
Whether or not the Internet of Things lives up to its commonly described vision, it is clear that powerful data collection and fast, comprehensive data analysis can help improve inventory and supply chain management.
A RocketDataScience.org article points out that Amazon’s proposed use of delivery drones could significantly benefit from true “just-in-time” fulfillment and goes on to say that “descriptive analytics (hindsight) tells you what has already happened in your supply chain. If there was a deficiency or problem somewhere, then you can react to that event. But, that is “old school” supply chain management. Modern analytics is predictive (foresight), allowing you to predict where the need will occur (in advance) so that you can proactively deliver products and services at the point of need, just in time.”
The future with AI
It is safe to say that the sea of discussion, articles, sales pitches, and seminars based on terms such as “Big Data”, “Data Analytics”, and “AI” contains a lot of hype. The AI/data capable company, research facility, educational organization, or consumer will benefit by finding those game-changing concepts which most certainly float in this ocean of thought.
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