2017 trends to watch: artificial intelligence - ovum -...

2017 Trends to Watch: Artificial Intelligence

The impact of deep learning in verticals and Internet of Things

Publication Date: 26 Jan 2017 | Product code: IT0014-003200

Michael Azoff


Summary

CatalystDeep learning (DL) has dramatically improved the capability of artificial intelligence (AI) systems in

recent years. We expect to see two main developments in 2017. First is the wider application of AI

systems in various domains, such as healthcare, agriculture, telecommunications, retail, and finance,

as well as in combination with emergent technologies such as Internet of Things (IoT) systems and

analytics for big data. Second are the new hardware accelerators due to appear in 2017 that are likely

to further improve the algorithms. The impact that AI will have on society is beginning to be

addressed. 2016 saw a lot of activity in the US at federal level and it is likely that similar initiatives will

begin in other countries.

Ovum viewDL neural networks within the machine learning branch of AI represent the most successful innovation

yet achieved in the field of AI. The moment in March 2016 when Google DeepMind’s AlphaGo

machine (based on DL) beat world Go champion Lee Sedol four games to one was a milestone in

human history. This AI technology will permeate many application areas in 2017, ranging from

autonomous driving to a wide variety of Internet of Things applications, from consumer products to

healthcare. IoT in particular will generate big data too vast for humans to process and AI will play a

major role in analyzing and making sense of streaming data and content in data lakes.

Enterprises and vendors alike need to address the implications of AI in their sector in 2017 to meet

the challenges and opportunities that arise from what is likely to be the largest and most profound

technology wave yet. Putting together a beginning strategy for AI should be on every organization’s

agenda for 2017.

The key technological innovation today that DL introduces is powerful self-learning. This means that

the designer and developer of the intelligent machine does not need to be an expert in the application

domain when building the AI system because the AI system learns to acquire new skills through the

DL training algorithms. These AI systems can also continuously learn and improve when used in real

work scenarios. A good example is AI-powered chatbots that are also assisted by humans. The

chatbots are learning from human intervention, and one can surmise that one day chatbots will run

autonomously.

Key messages Enterprises will start to plan for AI in 2017.

Diverse AI applications will appear in 2017 and IoT will be a driver.

An AI chip arms race will be sparked in 2017.

© Ovum. All rights reserved. Unauthorized reproduction prohibited.


Recommendations

Recommendations for enterprisesThe disruption that digital transformation is creating is unfolding on multiple fronts. How we build and

work with software is changing through agile, DevOps, and cloud native technologies. IOT is

connecting machines and humans, opening up many new opportunities in driving efficiency and

reducing waste. AI will transform how we make sense of data and will create new ways of working.

Enterprises must build a holistic view of these transformative changes to plan for a future that will

redraw the boundaries of who they do business with and how they conduct business. It will redefine

the value chain and relationships with partners and suppliers. This is the fourth industrial revolution

(after steam, electricity, electronics/computers, the fourth is cyber/AI). This revolution will make every

business a software-centric business. AI will be the largest disruptor in this new phase of change. This

report provides guidance on forming an AI strategy, and we believe 2017 should be the year to form

such a strategy if you have not already done so.

Recommendations for vendorsThe AI market is currently a land grab for start-ups with pioneering technology. While more

acquisitions are taking place as incumbent players acquire the new innovators, a continual stream of

fresh AI startups is appearing. We expect acquisitions to continue in 2017 as the major players

position themselves for an AI future. All vendors need to assess how AI will impact their business and

need to devise a strategy to survive the massive disruption that will take place.

Business trends and technology enablers

The impact of DL in diverse applicationsTable 1: 2017 outlook: AI-powered automationMonitor the business environment AI systems, and particularly DL technology, will see greater

use in applications and services. Internet of Things will be a major driver.

Create the technology portfolio Organizations will need to assess opportunities and form a strategy for data gathering and processing, and initiate proof-of-concept trials for building AI-powered solutions.

Select solutions and services Building AI systems will require data scientists and the use of hardware accelerators. Options for AI model building include creating in-house expertise or using service providers.

Manage deployment outcomes Organizations will need to keep track of the pace of innovationin the AI field which is expected to evolve rapidly in the decadeahead. AI systems built for consumer interaction need to be monitored carefully against abuse by malicious users.

Source: Ovum



Enterprises will start to plan for AI in 2017

AI technology will begin to impact businesses sooner than expectedThe subfield of AI known as deep learning neural networks is currently setting the pace and agenda

and is the focus of this report. We also refer to AI systems as advanced multi-system AI architectures

embrace hybrid combinations of AI subfields.

The pace of change is such that business will need to plan for AI in their domain in 2017. Without

planning for the potential impact of AI, businesses may be creating solutions that will become legacy

overnight when AI-powered start-ups appear in their domain. For instance, in the legal field the

example of eDiscovery has proved to be ideal for AI-powered algorithms. The job that used to be

given to trainee lawyers is now handed over to machines, and start-ups such as NextLP, NextLaw

Labs, Ross Intelligence, and eBrevia are bringing change to how law is practiced. In general, AI

systems may replace, augment, or assist existing business solutions. A whole spectrum of possibilities

therefore exist.

London-based Google DeepMind applied its technology to optimize the cooling system of Google data

centers. DeepMind’s AI system gathered sensor data on temperatures, the number of open windows,

cooling fan speeds, the routing of data traffic through networks, and workloads on individual

machines, and trained it to reduce cooling energy consumption by controlling these variables. It

achieved a 40% reduction in cooling energy consumption, with a 15% reduction in the overall cost of

running a data center. Figure 1 shows how energy consumption drops when the machine learning

(ML) system is switched on and then off.

Figure 1: DeepMind’s data center optimization at Google

Source: Google DeepMind

Google DeepMind is working with the UK’s National Health Service on big data projects, including, for

example, to analyze CT and MRI scans and identify cancer.

The advantages that AI machines bring are summarized as follows:



AI systems continuously learn.

AI powered machines can run 24x7 and can reduce running costs of physical systems.

AI systems can provide insights for humans to further explore.

When one AI system learns, all AI systems in the network learn.

AI systems can work in hierarchies, so that a highly intelligent manager AI system can control

less intelligent robots (virtual or physical).

New-generation physical robots are designed to be safe working alongside humans, and an

application area likely to grow will be care of the elderly.

AI systems introduce both opportunities and challenges (see Figure 2). Organizations must assess

how AI will impact their domain. There will be benefits and rewards, as well as risks and costs. The

goal for every business is to produce a strategy that maximizes the former and reduces the latter.

Figure 2: AI: Opportunities and challenges

Source: Ovum

Barriers to entry are being rapidly reducedHistorically, AI skills sets have been isolated in academia, but the pattern in recent years has seen

high-technology firms take a huge interest in AI and invest billions of dollars. With the openness and

sharing of fundamental research, there is a fundamental difference in how the research is conducted

today compared with the pre-Internet era. The culture of sharing of code and research papers is part

of the open source software ethos. The fundamental machine learning and deep learning libraries of

algorithms are shared openly to encourage progress, and it is recognized by all players that we are

just on the brink of innovation in AI. While today’s state of innovation is already making an impact,

more is needed to bring the art to fruition and achieve artificial general intelligence.

One can categorize the level of autonomy that AI systems introduce in general applications, in a

similar way that the Society of Automotive Engineers categorizes levels of autonomous driving, and

note that this applies to DL systems possible to build today.

Level 0: AI system providing assistance to humans.

Level 1: AI system in control but a human is continuously monitoring and ready to take over.



Level 2: Autonomous AI system with human alert at all times but having less to do.

Level 3: Fully autonomous system in areas of narrow application with a human present but

not needed to continuously monitor.

Level 4: Fully autonomous system except in severe/extreme conditions when a human needs

to take over.

Level 5: No human intervention required.

There are essentially three ways to access knowledge of (or services powered by) deep learning:

The machine learning services on public clouds (for example, Amazon AWS, Google Cloud,

IBM Bluemix, and Microsoft Azure) provide some access, although mainly traditional machine

learning, deep learning is available in some cases. Data scientists are the typical target

audience.

DL open source libraries allow neural network experts to jump start their neural network

modeling.

Start-ups that provide out-of-the-box capability powered by DL as part of their main offering.

Also worth mentioning is that IBM Watson has a number of services available on IBM Bluemix and

has a new business division devoted to Watson consulting projects. These will be bespoke solutions

that IBM will work on with the client, and models created from these projects will usually trickle down

into some form of generalized services on Watson Bluemix.

Putting together a strategy for AIWe recommend organizations write a white paper for internal use that evaluates the potential of AI in

various application areas, such as big data analysis, natural speech understanding, speech

translation, and system optimization.

In addition, it will be useful to look at forecasting the growth in AI improvement and how this will

impact your domain. AI systems are likely to continue to evolve as new AI hardware accelerators enter

the market and create new opportunities and improvements in AI algorithms.

The next step is to decide on what type of AI software capability to create in your organization:

Create in-house team/hire people

Make acquisition(s)

Partner

Use supplier.

There is a need for hardware acceleration to drive DL and so hardware requirements need to be

assessed. Building DL models requires data and so we recommend you appoint one or more data

scientists to manage the gathering of data, ensuring its high quality, maintaining it, and then building

solutions.



Figure 3: Summary of AI strategy steps

Source: Ovum

Once you have developed some proficiency in AI it will prove valuable to your organization to create

an AI center of excellence that can spread the expertise across the different verticals that may exist

within your business.

Finally, we expect legislation to support AI activity and monitoring will be necessary to remain

compliant.

Diverse AI applications will appear in 2017

Autonomous driving is a key application area for AI/DLPublic transport services are providing the first instances of autonomous driving on public streets, with

three examples in 2016: Uber launched custom XC90s supervised by humans in Pittsburgh, US,

nuTonomy launched a taxi service in a sector in Singapore, and Navya Arma runs autonomous

electric buses in Lyon, France.

We expect to see more autonomous driving public transport trials to appear across cities in 2017.

Many of these systems are powered by Nvidia Drive PX2, an open AI car computing platform

designed for auto manufacturers and their tier-1 suppliers who can pick how much of the platform to

use, or provide their own custom solutions. PX2 creates a 3D high-definition 360-degree map of its

environment, combining data from multiple cameras, LIDAR, RADAR, and ultrasonic sensors. DL

neural networks then process the data within PX2 to detect and classify objects.

Healthcare is one of the sectors most ripe for disruption by AIThe healthcare industry is a sector that will be transformed by AI, whether it is healthcare in hospitals,

local surgeries, or the home. Digital health assistant apps on smartphones are available today that

can take images of ailments, have an AI system on the cloud classify and diagnose the ailment, and



then refer to a doctor who may prescribe a treatment and can provide advice. Examples are Babylon

Health, Cellscope, and AliveCor Kardia.

Drug discovery is another highly active area for AI, with TwoXAR an example of this. Berg Health aims

to reduce the cost of developing treatments by putting sensors in patient drug trials, with the

generated big data then processed by AI.

Health management solutions from AiCure aim to understand the link between health and medicine,

and WellTok uses IBM Watson for health optimization.

Interpreting medical scan images using AI is being conducted by a number of ventures. Google

DeepMind is working with the UK’s NHS, Arterys has a non-invasive blood flow measurement

technology, and VisExcell is used for breast cancer detection.

The telecom industry is also ripe for disruption by AIAI can be used for managing telecom networks in several areas.

Orchestration: A fully NFV-enabled network will ultimately be controlled by a single NFV

orchestrator (NFVO). Accurately predicting network trends could lead to significant

improvements in network health and could significantly improve user experience.

SDN controller: Traffic through telco networks will ultimately be controlled by a centralized

SDN controller that may be augmented by AI functionality. This will allow the efficient and

proactive routing of traffic so that network outages are minimized and faults bypassed.

Analytics: Understanding user behavior or network status. Marketing can position their pricing

tiers and service plans and ensure that customers are satisfied.

Network deployment and optimization: Self-optimizing networks (SONs) are a key pillar for 5G

and NFV networks. AI may be used to continuously optimize the configuration of a telco

network according to traffic volumes, user behavior, and other parameters. Network

deployments may also be further improved by AI, which will be used to predict traffic patterns

and forecast user trends.

Another application area in telecoms is the use of intelligent agents, also called digital assistants or

chatbots. Digital assistants have been in existence for some years but we are concerned here with the

new generation powered by AI. Sophisticated chatbots listen to speech, perform natural language

understanding, and respond with a simulated human-like voice (there are also purely text-based

chatbots), and are used to provide an automated service for answering consumer questions or first-

line customer support services. The speech recognition and natural language processing in these

machines has improved dramatically in the last few years.

A good example is the recently launched Lex service from Amazon AWS that also powers Amazon

Alexa. It is described by Amazon as a third-generation chatbot technology. The latest generation is

intent-oriented after machine-oriented and control-oriented. This means the chatbot understands

concepts like booking a flight and will enter into a series of conversations designed to facilitate your

intent. Google has developed a sentence-parsing tool called Parsey McParseface which can identify

subjects, objects, verbs, and other grammatical building blocks with up to 94% accuracy. New

products such as Amazon Echo and Google Home bring chatbots into the home, and can save time

with use of voice activated commands to answer simple queries, such as “what is the weather?” This



technology will be a useful alternative to the “head on screen” that consumes much of our time these

days. This market will also provide vendors with access to a new data source for marketing purposes.

Two further examples of services that provide ready-to-use chatbots are IPSoft Amelia and Nuance

Nina. IPSoft Amelia is described as a cognitive agent that converses in natural language, engages

emotionally, can talk in multiple languages, understands context, and is easy to scale. Nuance Nina is

similar to Amelia and has been deployed at Swedbank where it processes about 30,000

conversations per month. It resolved 78% of first-contact queries in its first three months, and one

agent can handle 350 customers simultaneously. The technology requires responses in real time, so

humans can achieve fast processing by predicting what a person talking to them is going to say.

Chatbots will need this capability and it is expected they will continue to evolve.

IoT will drive disruption by AI in 2017Connected systems will drive AI adoption because big data is generated from sensors and will need

intelligent automation to process and make sense of the data. Applications exist in diverse areas

including retail, finance, and technology such as application and data center performance

management and optimization where vast amounts of data are generated by sensors. IoT will drive

remote servicing and AI will provide a means of making sense of diagnostic data.

The IoT market is highly fragmented and early adopters risk building legacy systems before open

standards emerge. We therefore expect organizations to make strong ROI cases when investing in

IoT technology. AI will help in exploiting IoT and will strengthen the ROI case.

The impact of AI on the job marketAI is likely to impact the job market as the debate rages over whether AI introduces a zero-sum game

scenario where a job gained by an intelligent machine is a job lost by a human. Ovum takes the view

that while the job market will change as a consequence of AI-powered automation introducing better

efficiencies and productivity, this will open up new possibilities for human work beyond the new jobs

that will appear to create, repair/maintain, and administer AI machines. The evidence for this lies with

the inefficiencies and waste that exist in most businesses, coupled with often poor customer service.

Yet the customer often has little choice but to continue to transact with these businesses. AI-powered

automation has the potential to remove much waste and inefficiency, creating a leaner economy, and

the businesses that survive will be differentiated by how they understand and treat their customers

with highly “people skills” activities that will make humans with these skills in demand.

Humans will be needed to steer AI machine activities, set goals, provide data, training, and monitor

machine activities and performance. The zero-sum game is a false expectation, and there will still be

new jobs created that require people to work with machines. AI technology is still a long way from

achieving artificial general intelligence (AGI) that matches humans in every one of our cognitive

capabilities, and society still has plenty of time to debate the consequences of that possible

eventuality.

Ovum expects other countries to follow the example of the US in conducting AI implication studiesTwo bodies working under the executive office of the president of the US, the National Science and

Technology Council and the Networking and Information Technology Research and Development



Subcommittee, in October 2016 published two White Papers on the impact of AI: “The National

Artificial Intelligence Research and Development Strategic Plan” and “Preparing for the Future of

Artificial Intelligence”. The papers offer a high-level framework for identifying scientific and technology

needs in AI, and a program for tracking progress to fill these needs and maximize the opportunity that

AI offers.

Both highlight the priorities for US federally funded AI research.

Make long-term investments in AI research.

Focus on human-machine interfaces, because most (it is assumed) AI systems are likely to

collaborate with humans rather than replace them.

Understand the ethical, legal, and societal implications of AI.

Create safe and secure AI systems.

Develop shared public datasets for AI training and testing to support industry AI efforts.

Establish standards and benchmarks by which AI systems can be measured and evaluated.

Understand the national AI R&D workforce needs.

The reports together paint a positive assessment of what AI can potentially offer and the benefits as

well as risks in using the technology. The idea is to work for the former and mitigate the latter.

An AI chip arms race will spark in 2017

New entrants will challenge Nvidia the market leaderThe market for GPU accelerators took off when general-purpose processing-capable GPUs made a

pivotal difference to DL. Hardware acceleration is necessary in DL training, which reduces the training

time by as much as two orders of magnitude (factor of 100). Nvidia has made addressing the DL

market its new company mission. While the recent high-end GPU market was worth in the million-

dollar range, the anticipation of embedded DL systems in, for example, vehicles and other connected

products, will size this market in the billion-dollar range. Clearly Nvidia will face competition with such

a prospect, and will not rest but continue to innovate. New entrants will become active in 2017 and

products have already emerged out of stealth mode to reveal themselves.

The following list of hardware accelerator firms is not exhaustive but covers the major players to look

out for in 2017:

AMD has GPUs and is beginning to address DL and has an Nvidia CUDA conversion kit.

Graphcore has an intelligent processing unit appliance due for launch 2017 that will

accelerate running frameworks such as TensorFlow and MXNet at a low cost.

IBM is conducting research on AI chips and still at the basic research level with, for example,

Synapse and TrueNorth.

Intel has Xeon Phi. It acquired Saffron in 2015 for its cognitive computing platform, and

Nervana in 2016 for its AI accelerator and its Neon Deep Neural Network software. The

Nervana Engine, renamed project Lake Crest, will be a new accelerator for DL training,

expected in 2017.



Nvidia has GPUs. Nvidia is the market leader in applying GPUs to deep learning, with its

most advanced GPU based on the Pascal architecture. Its next-generation architecture, Volta,

will be released in 2017.

KnuEdge has KnuPath Hermosa, a machine learning accelerator to power applications in

areas such as natural speech understanding.

Wave Computing has dataflow technology, the Wave Computing DL Computer, for running

TensorFlow DL software.

Appendix

Defining AI and artificial general intelligence (AGI)AI is a wide field and this report is concerned with a branch called machine learning and a sub-branch

called deep learning (DL) neural networks.

Talk of AI tends to be conflated in the media with AGI (a machine possessing the spectrum of human

cognitive skills). While DL state-of-the-art is able to achieve, for example, natural language

understanding that is better than human capabilities, these are still narrow applications areas. AGI

remains a goal of research and is still far from being accomplished, requiring significant milestones to

be achieved:

1.Accumulated learning and memory: A common knowledge of the world and how it functions

(such as the laws of physics).

2.Rapid learning: While babies are able to learn and generalize after a few examples, DL

requires big data proportions of examples. Sometimes this data can be self-generated by the

AI system, such as, for example, when learning to play a game, each trial of the game when

the system plays against different versions of itself will generate the necessary data. But

whichever, the data quantities need to be large. The goal is to create learning algorithms that

can build on top of existing knowledge rather than starting afresh, which is how most DL

systems are trained. Therefore rapid learning solutions will need the first milestone to be

fulfilled.

3.Prediction: Research shows that humans use prediction in many everyday functions, such

as listening to a speaker and predicting the next word they will say. Prediction is also linked to

imagination, and imagination is linked to creativity. If cognitive machines are to converse with

humans in real time without noticeable delays, they will need to possess powers of prediction.

4.Morality: A degree of moral and ethical understanding will be necessary to build into

machines, such as, for example, in safety-critical environments where fatalities may occur.

5.Quasi-consciousness: AGI will require machines to possess a sense of self and others.

6.Curiosity drive: Human thirst for knowledge drives mankind to explore and understand

nature. Similarly, AGI will need a curiosity drive if intelligent machines are to equal or better

humans in their ability to invent and research the nature of the universe.

Further reading AI/MLA:



How to Get Started with Artificial Intelligence: A Guide for Enterprises, IT0014-003161

(November 2016)

2017 Trends to Watch: Big Data, IT0014-003164 (November 2016)

“Making artificial intelligence applications safe for humans” , IT0022-000801, October 2016.

“Nvidia announces the most advanced AI computer in a SOC: Xavier”, IT0022-000800 ,

(October) 2016

“Google leads the way on AI acquisitions”, TE0004-001113 (October 2016)

“Can AI operate telco networks?”, TE0006-001264 (July 2016)

“The next chip arms race will be to power machine learning”, IT0022-000725 (June 2016)

“Nvidia bets on deep learning” , IT0022-000675 (April 2016)

“DeepMind AlphaGo and general artificial intelligence: are we there yet?” IT0022-000653

(March 2016)

“Google DeepMind achieves artificial intelligence (AI) milestone”, IT0022-000639, (March

2016)

“Learning fast with artificial intelligence”, IT0014-003147 (January 2016)

Digital Economy 2025: Technology Outlook, TE0009-001466 (October 2015)

“Machine learning in business use cases: Artificial intelligence solutions that can be applied

today”, IT0022-000335 (April 2015)

Chatbots:

“Consumer-facing intelligent agents have promise – but beware the hype”, TE0003-000968

(October 2016)

“Intelligent agents in consumer commerce must be handled with care”, IT0059-000075

(October 2016)

Intelligent Agents in Consumer Commerce: Commercial Prospects, TE0003-000966

(November 2016)

Intelligent Agents in Consumer Commerce: Market Dynamics, TE0003-000965 (November

2016)

Intelligent Agents in Consumer Commerce: Market Context, TE0003-000964 (October 2016)

AuthorMichael Azoff, Principal Analyst, Ovum Information Management Group

[email protected]

Ovum ConsultingWe hope that this analysis will help you make informed and imaginative business decisions. If you

have further requirements, Ovum’s consulting team may be able to help you. For more information

about Ovum’s consulting capabilities, please contact us directly at [email protected].


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