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Three AI breakthroughs and what they mean for business

Artificial intelligence (AI) is an area where management is certainly encouraged to have a strategy, but it can be bewildering to stay on top of what’s happening in the space and try to understand how the technology can be applicable in a business setting- the boardroom can feel quite far from the lab!

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Published by Questex Asia
on 11 Jul 2019

Three AI breakthroughs and what they mean for business

CEOs are confronted daily with the speed of technology evolution, under pressure to transform organizations to ensure they stay competitive, relevant and prepared for the next wave of change.

Business leaders are also challenged to be informed about new technology and resources and determine which are best for their organizations while also maintaining company culture and improving productivity.

Artificial intelligence (AI) is an area where management is certainly encouraged to have a strategy, but it can be bewildering to stay on top of what’s happening in the space and try to understand how the technology can be applicable in a business setting- the boardroom can feel quite far from the lab!

Several recent AI breakthroughs are already available for businesses to apply. Here are three recent AI advancements- all in the area of deep learning, the most advanced branch of AI- and how they can be applied to business.

Deep learning in visual recognition

Before 2012, AI was effective in certain scenarios such as detecting faces and recognizing the side-view of cars. However, at that time, the typical amount of visual data available was small- only a few thousand images had been labelled and categorized.

2012 was the breakthrough year in visual recognition demonstrated by ImageNet- a robust data set of 14 million+ images in more than 21,000 categories, developed from 2007-2010 by a team of scientists including me. This development showed that computers can leverage huge amounts of training data to improve visual recognition performance towards that of humans.

By 2015, computers were able to surpass human performance in recognizing 1000 object categories in ImageNet.

There are a number of exciting ways that industries such as transportation and healthcare are using this technology- to better perceive the environment surrounding an autonomous vehicle, and better identify anomalies such as tumours and plan the subsequent treatment.

For traditional businesses - particularly those that are ‘document-heavy’ such as banking, insurance and law - visual recognition powered by deep learning can identify huge numbers of documents at speed, digitize and categorize them.

Where a human must add information manually, a computer can review and file several thousand (or more) documents an hour and can flag a human reviewer to verify the information only for those that might be distorted or particularly complex. This frees up time for human workers to focus on more critical and creative tasks.

Reinforcement learning

In 2016, world champion Go player Lee Sedol was beaten at the game by Google DeepMind’s AlphaGo computer program. Go is a strategic game that requires players to place their game pieces (stones) in the right area of the board to stop the other player advancing.

The technology was able to do this because it had practised millions of times to improve its win rate through reinforcement learning, which looks at which actions software should take in a given environment or situation to result in a reward or positive outcome (in the case of AlphaGo, winning the game).

For businesses, this breakthrough can support an improvement in areas that involve resource allocation.

For example, large technology companies with large data centres need to ensure consistent quality while also reducing power consumption. Reinforcement learning can automatically allocate which machines should perform a task, while also changing the appropriate cooling settings at the same time. As in the game of Go, it’s about ‘where to place to stone’ for the best outcome.

Reinforcement learning can also be valuable for any company that has a logistics component within its business. In the case of delivering or transporting goods or people (shipping companies, rideshare services or food delivery providers, for example), organizations have typically made resource allocation decisions by looking at past patterns and experience.

However, by adding reinforcement learning, these businesses can now predict future resource allocation, ensuring the best action for any given situation (i.e. more drivers or riders in times of heavy traffic, high demand, etc.).

Deep learning model for language recognition

Like visual recognition, it’s only in recent years that AI has become able to understand raw text in multiple languages; pair similar words together; and identify when the same words have multiple meanings (such as ‘Let’s go out’ vs. ‘Let’s play Go’).

This capability opens a wide range of possibilities for business leaders what want to better understand how customers, staff or other stakeholders interact with written information about or relevant to the organization.

One area of business where this can be particularly valuable is marketing. Marketers are adept at collecting data on consumers’ digital footprint- what they search, where they browse and shop, etc.

Traditionally, a marketer sees that a customer visits travel, fashion website and finance websites, setting broad categories for her interests. With improved language recognition, marketers can gather more insights from the text a consumer is seeing when she lands on a web page.

The technology can ‘read’ specific words, drawing out correlating terms such as ‘fashion show’, ‘Paris’, and ‘air miles rewards’. With this insight, marketers can more effectively target content and messages.

What it all means

Th technology reviewed here already features in business-ready solutions, and executives in the midst of making decisions about AI should confirm that their solution providers are using the most cutting-edge AI technology available.

There will be many more advancements in AI, but business leaders cannot wait to make decisions to ensure they have the ‘latest’ thing. Rather, they need to work with technology and talent that have the flexibility to adapt and upgrade as AI advances.

Senior management themselves should find ways - through lots of reading and listening and working with educated members of staff and outside experts - to stay abreast of what’s happening in AI in the lab, so that when that technology reaches the boardroom, the C-suite can make the most effective decisions about implementation to drive the business forward.

Last Modified Date: 11 Jul 2019