It is not much better to solve real-world problems than today’s AI ancestors. Expert systems, neural networks, and estimations of common intelligence are everywhere. Venture Capitalists are funded to launch the magic letters that are attached to their PowerPoint presentations with “AI”. Consultants and chief executives will announce the need to embrace Artificial Intelligence and its sibling “Big Data“. We’ve seen this movie before.
Fifty years ago, “chatterbots” such as Eliza and Prayer were declared successful in March for intellectual intelligence. Complex neural networks are considered the oldest but reasonable models of human brain activities. Behind the intense interest in AI, Ridley Scott’s 1982 film “Blade Runner” inspired artificial life. It forms a dystopian future beyond human intelligence and strength.
Artificial Intelligence: Winter Is Coming Soon
Absolutely humanity is about to give birth to a digital Adam and Eve? However, it did not. We did not go one byte in understanding human intelligence. Moor’s law is very thankful for the law. But the underlying algorithms are roughly equivalent to the machines that worked almost 40 years ago. Instead, we rebranded those algorithms creatively.
Deep Practice:
The good old fashioned “data” suddenly became “big”. The vintage neural networks of the 1970s began to provide a fascinating phenomenon of “deep practice”. But IBM’s supercomputer did not beat human competitors in the Watson television show Jeopardy?
Is it really revolutionary?
Watson was a commercial success. The IBM internal documents reported that Watson recommended “safe and inappropriate” cancer treatments. It’s a bit harder than IBM’s idea of going to the diagnosis of cancer with a jeopardy. Google with another competitor had the Deep Mind for AI crown. In a salute of IBM strategy to throw hundreds of engineers at the sleek successes in the definitive achievements, DeepMind’s oldest strategy game is the world’s top player trumpeted in its AlphaGo program’s success.
Deep Blue, a 1997 chess-playing computer that defeated Gary Kasparov, or Watson. It had some success. But, DeepMind tried to extract commercial value. With many fans, the UK’s National Health Service has appointed Deep Mind in 2015. It is to improve patient outcomes and reducing costs. Three years later, the audit of the Deepmind project for acute kidney injury called the Streams, organized by the legal company Linklaters, said: “We can not imagine the concept of a breakdown of the streams without any disrespect to the Deepmind”.
DeepMind offers NIFT graphical interfaces for existing NHS algorithms. The truth is that their ancestors have solved the real world complex issues more than three decades ago. The AI’s latest incarnations are not very good. Heavily tuned systems can succeed in narrow challenges like Go, Chess or Jeopardy. But the human being can present a diagnosis from many and the contrary characteristics beyond the present.
What is AI good?
Identifying patterns in complex data is always good. Medical Image Anemic Detection, Hydrocarbon Detection, Consumer Behavioral Prediction, and Fraud Identification have been developed in all computational capacities. Both share two things: well-structured input data and large volumes of well-defined endpoints.
How could companies benefit?
First, we need to stop investing in regular AI and large data projects. Consultants prefer this, but most of the investment is waste. We will ask in my technology company: “How does the CEO add value to five minutes or less?” If the pitch lacks a clear business case, it’s definitely not the pursuing value. Secondly, major executives must drive AI projects individually. A recipe for delegation’s failure. Such formulas apply to investments in AI. Investors should stop funding companies. Most of the companies buy only the off-the-shelf AI “tools” if the rebrand pre-existing algorithms are AI.
Investors should focus on basic questions. What problem can this use of AI solve? How do you measure results? How does the AI provider take over its stake? If an investment is successful, these questions should be answered. The Blade Runner 2049 was released last year. It is the 1982 film’s sequel, an active hype around the AI.
There is a picture of the ancestors of a mental dystopian future. Humans are replaced by patient robots. American Association for Artificial Intelligence In 1984, “AI Blade Runner”, Deckard, two years later was termed as “AI Winter”. It was used to describe the dramatic slowdown in interest and investment. Love with artificial lifestyle as “Rachael”.
