Four Trends in Artificial Intelligence That Affect Enterprises

We all have seen the headlines: “Google’s AlphaGo
defeats world-class Chinese “Go” player”; “IBM’s Watson is tackling
healthcare with artificial intelligence”; and so on. Artificial
intelligence (AI), which is essentially a set of tools and programs that make
software ’smarter’ in a way an outside observer thinks the output is generated
by a human, is starting to break out on the global stage.

For most in Asia, AI in its most basic forms is
already evident in daily lives. Personalized product recommendations on
e-commerce platforms like Taobao or Flipkart and voice assistants like Siri or
Google Now are just a couple of examples. Still, far-reaching changes lie
ahead. With exponential growth in computing power and favorable supplyside factors,
like the advent of advanced algorithms, a vast pool of indigenous AI-related
talent and massive government-funded infrastructure development, both the
breadth and depth of AI adoption in Asia is set to sharply accelerate in the
coming decade, creating an economic value between USD 1.8trn and USD 3trn
a year by 2030 in the region.

Closer to home in Singapore, the government,
through the National Research Foundation (NRF) will be investing up to S$150
million into a new national programme aimed at boosting the nation’s artificial
intelligence (AI) capabilities over the next five years. The initiative
labelled as AI.SG, will see more collaboration with companies and startups to
power the country’s AI efforts.

Enterprises forecasted to be most affected by
AI in Asia include: financial services, healthcare, manufacturing, retail and
transportation. Andrew Ng, the renowned data scientist, has said that
artificial intelligence (AI) needs to be a company-wide strategic decision.
Companies that don’t strategically invest in AI will slowly lose market share
to companies whose core businesses are built around AI.

For enterprises, there
are four trends in artificial intelligence that stand out: large-scale machine
learning, deep learning, human-enhanced AI and autonomous systems. Contributing
to these trends are less expensive and more powerful hardware, and open source end-to-end
connected data platforms that maximize the value of data-in-motion and securely
store, manage and perform complex processing of data-at-rest. Furthermore, pure and hybrid cloud
deployments enable companies to quickly scale and access additional resources
on demand.

Here’s a bit more on
the four trends in artificial intelligence that affect enterprises.

1.
Large-scale machine learning

The ability to learn without
being explicitly programmed, Machine Learning, has been around for a long time
and is well understood. What is different is the relatively recent emergence of
general purpose tools, such as Apache Spark, that enable processing of very large datasets. Additionally, data
scientists can now collaborate and rapidly deliver high-impact and high-value
business assets, without worrying about managing compute resources, security,
or data-replication.

A classic example of machine learning is
detecting fraudulent login attempts. Instead of explicitly specifying every
rule and every possible fraud case, machines learn by being presented with
thousands of examples. The advantage here is that once the initial model has
been created, it can continuously evolve and self-improve, becoming more
accurate.

2.
Deep learning

Due to recent improvements in computer graphic
cards and releases of popular frameworks, deep learning has some excellent
results in specific narrow use cases with actionable intelligence. This now
makes it possible for businesses to hone in on new business opportunities.
Additionally, raw processing costs are falling rapidly, lowering the barrier to
entry for everyone, and there are many pre-trained (downloadable) components
that allow companies to significantly shorten model training time and focus on
optimizing their networks for their specific use cases.

3.
Human-enhanced AI

Another common trend is having humans evaluate
results from AI. AI is still a long way from having humanlike abilities of
comprehension, reasoning and intuition. Human-AI teaming will result in better
outcomes than either alone would provide. For instance, in health care, using
the combination of AI and the human experience can reduce false positives and
increase patient satisfaction, which often leads to monetary gains.

4.
Autonomous systems

More and more systems operate and adapt to new
circumstances with little to no human control, changing the landscape of the
workforce and the way we think about the workforce moving forward. This
category is much broader than just autonomous cars or drone delivery. There’s
automated financial trading or automated content curation systems, such as
creating automated news digests around sports or finance. But more importantly,
driving the business includes the ability to diagnose and update internal
systems such as security vulnerabilities, which is key in the world of rapidly
evolving cybersecurity threats.

These four megatrends highlight the exciting areas of
innovation that will be impacting organizations large and small in the coming
years. Data science plays a vital role in unlocking the potential of enterprise
data to extract maximum value, improve revenue and increase profitability. To
hear more about these trends you can attend DataWorks Summit in Sydney Australia
September 20-21.