You don’t put some of the most intelligent people in the world to work on artificial intelligence just to win board games.
In the words of several experts from Google, Marketing analyses what AlphaGo AI’s on-going three-game matches between AlphaGo and the world’s first ranked Go player, Ke Jie, means to different businesses and industries.
Q: How has Alphago improved itself from last year’s challenge with the previous world champ Lee Sedol?
A: According to DeepMind’s co-founder and CEO Demis Hassabis, the latest version of AlphaGo runs on a single TPU (Tensor Processing Unit) machine. This leap in efficiency is possible through a new kind of chip Google revealed at its I/O developer conference last week. The current version of AlphaGo uses ten times less computing power than last year’s and learns much more quickly.
This means that the machine is now driven by a new and more powerful architecture, and that it can now learn the game almost entirely from playing against itself, relying less on data generated by humans.
Q: What roles would AlphaGo’s deep learning play in businesses?
Alphabet’s executive chairman Eric Schmidt said the new deep-learning capability could increases daily productivity and opens up countless opportunities for businesses, especially in fields of healthcare, transportation, and government.
On the healthcare font, for example, Google’s health research product manager Lily Peng said computer tools could help extend the screening of eye diseases to underserved countries, including India, which has a shortage of 127,000 eye doctors serving 1.3 billion people.
She noted Google’s machine learning models saw slightly higher accuracy in diagnosing patients than some U.S. board certified ophthalmologists. The potential of this technology extends to other disease detection as well. For example, Stanford researchers recently used TensorFlow, a machine learning repository on GitHub, to detect skin cancer from images.
A.I. techniques could also help address some of the most important socio-economic challenges. For instance, DeepMind co-founder and head of applied AI, Mustafa Suleyman, said DeepMind is recently working with Google to “reduce the power required to cool Google data centers by 40%.”
While this all sounds very promising, marketers don’t have to feel left out just yet. Building on Google’s announcement that it will start using machine learning to track customers to physical shops for better targeted advertising, alongside its continued reliance on ad dollars for revenue, it’s not a big leap of faith to think machine learning will be used for things like improved targeting, better customer profiling and all round better marketing mechanisms – including design, copy writing/checking and content customisation for different customers.
Q: Are there any examples of machine learning’s implications in consumer products?
Jia Li, Google’s head of Cloud and A.I. research and development, said she is also working to help businesses apply AI, including automobile companies and call centers, which use Google’s speech API.
Connecterra, a Belgian-based IoT startup, applies machine learning to dairy farming while Australian researchers use the technology to judge the health of sea cow populations.
In Google, Jeff Dean, the internet giant’s senior fellow, said machine learning techniques can remove raindrops from photos in Google photo, and add filters to stylize photos in the manner of famous works of art.
Zhifeng Chen, a Google software engineer, added that machine learning has powered recent improvements to the company’s translation platform. With the introduction of neural machine translation techniques, the quality of translations have significantly improved. Combined with “computer vision,” the Google Translation app can now instantly translate signs through a phone’s camera.