Each year brings the same amount of innovation as the previous several years -- and the human brain finds it hard to imagine the consequences. For perhaps the first time in history, technology is evolving faster than we expect it to. For example, experts estimated that it would take another decade before a computer would beat a world champion at the ancient and complex game of Go -- yet AlphaGo triumphed earlier this year. It's not hard to find parallels between how new technologies are changing the world today, and how they will affect our business systems in the future. For example, every Tesla car in hands-free driving mode gathers huge amounts of information about current conditions.

What does the Chinese Room problem attempt to demonstrate?

A study published last Monday, heralded as an historic achievement by Microsoft, details a new speech recognition technology that's able to transcribe conversational speech as well as humans -- or at least, as best as professional human transcriptionists (which is better than most humans). The technology scored a word error rate (WER) of 5.9%, which was lower than the 6.3% WER reported just last month. "[I]t's the lowest ever recorded against the industry standard Switchboard speech recognition task," Microsoft reports. The rate is the same as (or even lower than) the human professional transcriptionists who transcribed the same conversation. "We've reached human parity," says Xuedong Huang, Microsoft's chief speech scientist.

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It is a massive trial-and-error process. From time to time, you will hear about a new fantastic piece of computer science. For example, right now deep learning is the hot new thing. Some years ago, people were very excited about MapReduce. As an ecosystem changes, some tools become less likely to be useful while others gain dominance in common use cases.

I'm always interested in reading articles about new approaches in my field of artificial intelligence and machine learning, so the recent AdWeek post by Steven Wong and Derek Slater of Ready State suggesting that'Machine learning is about to turn the marketing world upside down' really caught my eye. Every time I hear or read that machine learning is'about to' do something it makes me smile, particularly in this case as'the marketing world' is completely relevant to my job. As Chief Science Officer for an international marketing company, I am working with these technologies on a daily basis. Unlike the 2017 scene set out in the original post, more marketing companies are having conversations about human designed personas compared to segments identified by machine intelligence. While correlation isn't necessarily causation, understanding the similarities in your customer base that lead to a purchase decision is powerful.

The developments in Google's Cloud Computing segment, especially the Cloud Machine Learning service, have been so rapid that Google calls it one of its fastest growing product areas. Google has been ramping up their Cloud Platform quite aggressively in recent months. Just a few weeks ago, the Google Cloud Platform opened its newest zone in Tokyo, increasing the total number of regions they are present in to six - three in the US and one each in Belgium and Taiwan and Tokyo. Not long ago, the company announced its acquisition of Orbitera, a cloud commerce company. The developments in Google's Cloud Computing segment, especially the Cloud Machine Learning service, have been so rapid that Google calls it one of its fastest growing product areas.

Just over five years ago, IBM's Watson supercomputer crushed opponents in the televised quiz show Jeopardy. It was hard to foresee then, but artificial intelligence is now permeating our daily lives. Since then, IBM has expanded the Watson brand to a cognitive computing package with hardware and software used to diagnose diseases, explore for oil and gas, run scientific computing models, and allow cars to drive autonomously. The company has now announced new AI hardware and software packages. The original Watson used advanced algorithms and natural language interfaces to find and narrate answers.

Autism spectrum disorder (ASD) is typically diagnosed in early childhood, but genetic detection of this brain disorder could mean more timely interventions that improve life for the patient and their carers. A new study suggests that machine learning algorithms might be used to analyze genetic data that points to an autism spectrum disorder diagnosis before symptoms become obvious. Fuad Alkoot of PAAET in Kuwait, and Abdullah Alqallaf of Kuwait University, Kuwait, explain that unlike other conditions, such as cancer, little heed has been taken to the possibility of early genetic detection of autism. This is despite the fact that an early diagnosis could be very useful to parents and carers. The team has now developed a four-stage computerized neural network system for testing simplified genetic data.

AI for short -- has already changed the way businesses operate. Its role in streamlining manufacturing and production is well-known. But AI is also transforming the way companies interact with customers. That's good news since the ability to consistently deliver an excellent customer experience is critical for companies in today's hyper-competitive economy. There are some incredibly exciting developments on the horizon as the IoT expands and AI tools become more advanced.