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Human-like AI quest drives general AI development efforts

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In this e-guide

Common sense AI

approaches point to more

general applications

Artificial intelligence creativity

tools mimic human ability

More curiosity could help

narrow AI tools handle

broader uses

New deep learning

techniques take center stage

Researchers race for

quantum AI as quantum

computing advances

Augmented intelligence: The

clearest path to focused AI?

Human-like AI quest drives general AI

development efforts

In this e-guide:

While AI technologies can do things humans can't, they're

currently limited to specific and straightforward tasks -- a state

known as narrow AI. A big goal -- or holy grail -- for developers

is to create human-like AI tools that think the way people do.

Estimates of how long it will take to make so-called general AI a

reality range from a few years to more than a century. Much

work lies ahead. "We will not get there using the techniques we

have today," Raj Minhas, head of the AI research lab at Xerox's

Palo Alto Research Center, told TechTarget contributor Maria

Korolov.

This guide compiles stories on the push toward human-like AI

and the challenges that AI vendors and users must overcome.


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Common sense AI approaches point to more general applications

Maria Korolov, Technology Journalist

Artificial general intelligence is the holy grail of AI research; this form of an AI

system can think for itself, has common sense, has a similar intelligence level to

humans and could even pass for a human in conversation.

AGI raises big questions about ethics and human employment, but the most

fundamental questions about AGI and how close we are to it have yet to be

answered.

In late 2018, in a book titled Architects of Intelligence, futurist Martin Ford

interviewed AI professionals who said that, on average, there was a 50%

chance that common sense AI would be completed by 2099. Google's Ray

Kurzweil put it at 2029. Rodney Brooks, co-founder of iRobot, was at the end of

the spectrum, predicting the year 2200.

Samir Hans, AI expert at Deloitte Risk and Financial Advisory, predicted that

we're going to see tangible results in two to three years. AI can already learn

from its mistakes, which means there's a feedback loop that improves the AI

over time.


https://searchenterpriseai.techtarget.com/feature/How-far-are-we-from-artificial-general-intelligence

https://searchenterpriseai.techtarget.com/definition/artificial-general-intelligence-AGI

http://book.mfordfuture.com/

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So, why the spectrum of possible application dates? Part of the problem is the

very definition of AGI and how we measure it.

What is intelligence?

There was a time when technology's ability to do mathematics or make logical

inferences was a sign of intelligence. As soon as calculators were invented, the

goal posts were moved. Where is the goal for AGI? Maybe it's the ability to play

chess. To parse human speech. To extract meaning from text. To translate from

one language to another. To play Jeopardy. To pass a Turing test. As AI hits

each of those milestones, it becomes clear that we still aren't seeing true AI in

its final form.

"My definition of general AI is where a machine is fully autonomous, performing

human tasks, without human involvement," said Josh Elliot, director of AI at

Booz Allen Hamilton Inc. "And you need the ability to perceive, and learn, and

act and the emotional side of what humans actually bring to the table."

Today, AI is primarily special-purpose machine learning systems and algorithms

that can do one thing well, Elliot said. This is narrow AI, and while it's getting

really good, AGI requires the ability to do tasks across multiple silos.

"We can get very good results in specific domains, but there is a huge gap,"

said Raj Minhas, head of the AI research lab at the Palo Alto Research Center.


https://searchenterpriseai.techtarget.com/definition/Turing-test

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Minhas said that he wouldn't even hazard a guess about whether technology

would ever achieve AGI.

"We will not get there using the techniques we have today," he added. "The

techniques we have are the ladders that allow us to climb skyscrapers, but they

won't get us to the moon."


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With each new advance in AI, the definition of AGI gets more nebulous. As

computers advance calculation, analysis and predictive abilities, the criteria for

real intelligence becomes more amorphous and includes feelings, self-

awareness, empathy and ethics.

"You can have a machine that's very adept at learning, but does it have the

ability to be sentient?" said Matt Jackson, VP of digital innovation services at

Insight, a consulting and system integration firm.

With the increase in available computational power, the emergence of quantum

computers and the improvements in AI algorithms, the progression to eventual

common sense AI is there.

"It will happen in a reasonable lifetime, 20 to 50 years, probably on the latter

end," Jackson added.

But a machine that can be useful and capable enough to pass a Turing test is a

lot closer, he said.

"If you take Siri or Alexa and think about how it can expand on the abilities [we]

have today, then you're effectively simulating general AI with multiple types of

narrow AI," he said. "I think we will have that in a decade or two."

Or maybe even sooner, according to some experts.


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"When I studied AI at university, I was taught that we would have completed AI

if it could beat a human being -- a grandmaster -- at the ancient Chinese game

of Go," said Rob Clyde, chair of the ISACA board of directors.

"Software could not brute-force it, like it can [in] tic-tac-toe, checkers or chess.

Two years ago, Google bought an AI that beat a grandmaster. The holy grail

has been reached. Since then, they have built self-learning AIs that learn by

playing themselves," he continued.

According to Clyde, with platforms like AlphaGo and Watson, the same AIs can

do many different things -- achieving some experts' definitions of AGI.

"I would argue that the tipping point has been reached. We've reached the point

where the growth is exponential. The pace is going to be incredible over the

next few years," Clyde added.

Progress measurement

Along with no clear definition of common sense AI, the AI industry also lacks

clear metrics for progress.

One common approach is to measure the success of AI algorithms at particular

tasks, such as image recognition or natural language processing. Here, AI

systems are quickly approaching -- or already exceeding -- human levels of

performance, and the rate of progress is accelerating.


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In 2017, AI programs matched or exceeded human performance at identifying

skin cancer, recognizing speech, and playing poker and arcade games. In 2018,

AI matched humans at tasks including translating Chinese to English and

grading prostate cancer. AI systems keep getting better at communicating with

humans. Last May, a new language benchmark test, General Language

Understanding Evaluation, was released. AIs scored at under 70% -- compared

to around 90% for humans. By October, AIs had already improved, with scores

crossing the 80% mark.

If AI can continuously improve, how do we measure progress vs. achievement?

Maybe AGI depends on common sense -- being able to explain what's going on

in a situation. Say, for example, looking at a picture and answering questions

about what's going on and why. But who sets the limit, the goal, and when is it

achieved?

What comes after common sense?

AGI will enable companies to move on from AI technology that currently exists

as narrow, special-purpose machine learning systems that are difficult to train

and calibrate.

Banks, for example, will be able to gauge customer emotions, identify special

needs cases, make more accurate predictions and better detect fraud, said

Raghav Nyapati, digital automation product strategist at Bank of America.


https://arxiv.org/pdf/1804.07461.pdf

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"In order for the machines to reach that state of general artificial intelligence, it

might take another 10 years. We are already seeing some of this, where

systems are able to discern a person's emotional state based on their voice or

facial recognition," Nyapati said.

With common sense AI, the Anderson Center for Autism could get answers to

questions it didn't know it needed to ask, said CIO Gregg Paulk. His center

currently uses HR tools from Ultimate Software to predict which of its most

valuable employees are most likely to leave the company early enough for the

organisation to take steps to improve their job satisfaction.

"If [AI] had common sense, it could identify areas where we can improve, such

as with tasks that we're doing on a daily basis," he said. "I think that would have

a huge reward. A lot of times, you don't know what you don't know."


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Artificial intelligence creativity tools mimic human ability

Ronald Schmelzer, Principal analyst

While conversation about artificial intelligence is centered on augmenting the

enterprise or adding value to daily lives, there is also unique interest and effort

in applying AI to creative human pursuits. While artificial intelligence creativity

might not seem relevant to enterprises, the use of machine learning to generate

images and video, craft text for ad copy, marketing materials, press releases

and speeches has definite impact in the business sector.

AI turns to text

AI systems that create and generate text of all forms have been used with

surprisingly good results. Early experiments in natural language generation

(NLG) were focused on converting numerical and quantitative data into a more

natural narrative form. In the past, this would have required human analysts to

spend hours poring over data to generate reports. However, AI-based systems

can now easily complete content summarization and report generation with very

similar levels of quality in just minutes.


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News organisations and media outlets -- ranging from Bloomberg, The

Washington Post, The New York Times and Forbes -- have been using NLG

systems to generate content for general consumption. Other companies in the

financial, insurance, legal and healthcare industries are adopting AI-based NLG

systems to generate content that can be consumed by nontechnical users. The

user-friendly content produced includes financial reports in human-readable text

form, synopses of healthcare records and data, and analysis of legal cases and

intellectual property research for legal workers.

Recently, OpenAI released a deep learning neural network model that can

generate entire paragraphs of coherent text from just a small block of

introductory words, phrases and facts. OpenAI's artificial intelligence creativity

platform focuses on maintaining coherence and accuracy at a large scale --

from 500 to thousands of words. Even in its limited released form, these

pretrained neural network models are proving to be very helpful in generating

longer-form content that would otherwise be the job of marketing, analysts or

communications teams in an organisation.

Generating audio, speech and music

Computer-generated speech has been around for decades -- however most of

that generated speech comes from a list of preprogrammed output responses,

or by combining words together from a preselected list. It's only recently that

speech and audio are being generated without manual interference by systems


https://searchenterpriseai.techtarget.com/definition/deep-learning-deep-neural-network

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that use AI and machine learning to generate good quality audio that mimics

human speech.

In May 2018, Google demonstrated this at their Google I/O event with a demo of

their Google Duplex intelligent assistant. The assistant was able to make phone

reservations for a haircut and book restaurant reservations with the human on

the other end of the call unaware they were speaking with a robot. The assistant

generated conversations, responded to questions, and elaborated on

information while using frequent human pauses, interjections and fillers.

In 2016, Adobe's VoCo preview showed ability to modify existing audio to say

things speakers didn't actually say; using existing clips to change intent. These

demos, still unfinished, show the power of AI-enabled voice and speech

generation that could be used for a wide range of practical purposes.

In addition to human speech, AI is enabling a high-quality generation of music

and other audio output. In 2018, YouTube personality Taryn Southern released

a record produced and composed entirely by AI. While Southern provided inputs

that controlled the feel of the music, the AI system was able to generate new

music by synthesizing existing tracks. The immediate application of this

technology is to produce original, royalty-free music for video background music

or other applications.


https://searchenterpriseai.techtarget.com/feature/Natural-language-processing-drives-conversational-AI-trends

https://searchenterpriseai.techtarget.com/feature/Natural-language-processing-drives-conversational-AI-trends

https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html

https://searchenterpriseai.techtarget.com/news/450422357/Creative-AI-looks-to-take-machines-into-uncharted-territory

https://www.youtube.com/watch?v=XUs6CznN8pw

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Does this photo exist?

Some of the most powerful examples of artificial intelligence creativity tools are

in the creation of images and video. Machine learning systems using deep

learning approaches are able to create lifelike images of people that don't

actually exist using GANS image compilation. Other AI-based systems can

create original advertising copy and images, generate original film trailers for

movies, and create new paintings mimicking and adhering to the style of notable

artists.

The applications of artificial intelligence creative technology in the enterprise

range from marketing departments generating original hyper-personalized

advertisements to healthcare organisations generating helpful videos tailored to

the specific needs of their patients. Whatever the intent, the use of machine

learning to create images and video is on a path from fringe use to mainstream

application.

The bigger question surrounding creative AI is, are these systems exhibiting

real creativity or simply mimicking the creative ability of humans? Where does

the line of adaptation and imitation end and creativity begin? While we seek the

metaphysical answers, AI-based systems remixing human creative output to

generate new results are proving to be extremely valuable for organisations in

generating text, images and audio they need without requiring the human labor

for creativity.


https://searchcontentmanagement.techtarget.com/tip/Using-AI-in-content-creation-can-streamline-publishing

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More curiosity could help narrow AI tools handle broader uses

Torsten Volk, Managing Research Director

Artificial intelligence has made significant strides in recent years, but it still has a

major constraint: narrowness.

The inherent limitation of AI today is its specificity. You can get a narrow AI

application to take orders at the McDonald's drive-thru or beat the world's best

chess and Go players. You can build AI software that drives cars with fewer

accidents than humans. You can also use AI for quality control in product

assembly, diagnosing cancer or prequalifying mortgage applicants. However,

the moment you change up the task even slightly, the AI tool is at a loss as to

how to respond.

To understand the specificity issue of AI, it is key to look at the underlying core

principle of how AI works today. The issue is rooted in the conceptual approach

of approximating human decision-making by combining sets of algorithms into a

model that is then rewarded for good responses to environmental stimuli and

punished for bad ones.


https://searchenterpriseai.techtarget.com/feature/AI-functionality-limited-today-but-could-be-a-game-changer

https://searchenterpriseai.techtarget.com/opinion/Few-artificial-intelligence-applications-live-up-to-the-name


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AI equation: Algorithms plus compute power

The narrow AI of today is based on sophisticated algorithms and incredible

amounts of brute force compute power. We define AI as an accumulation of

better and better algorithms relying on better and better hardware to calculate

their way out of narrowly defined challenges.

For example, when looking for a tumor on a CT image, an AI application sees

combinations of individual pictures that have no meaning by themselves.

However, different combinations of pixels in an image can be correlated with

different probabilities of cancer being present.

This is a fundamentally different approach from how a human doctor looks for

cancer. Instead of using abstract processing power, the doctor leverages her

experience, skill and intuition to look for indications of cancer where they are

most likely to occur. Ultimately, this can lead her to identify a few corner cases

of cancer and, hopefully, save some lives by doing so.

On the other hand, the AI application is relentless in examining all of the

available data, potentially yielding more thorough results than a human doctor.

But the same AI program that can identify the rarest corner cases of lung cancer

will not be able to diagnose bone cancer or even a broken leg because its set of

training images were strictly focused on lung cancer.


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Igniting the spark of curiosity in AI

Expert Go players are able to use sophisticated strategies and diversions when

playing the board game, but they can't think through every obscure set of moves

that is likely to lead to a game-winning trap much later down the road. An AI

program can.

In its 2016 match against human champion Lee Sedol, Google subsidiary

DeepMind Technologies Ltd.'s AlphaGo algorithm deployed a long-range game

plan that didn't produce any immediate measurable gains on the board.

However, DeepMind managed to incentivize the algorithm to explore

unconventional strategies that increased the probability of it gaining significant

advantages later in the game.

Of course, once AlphaGo used this spark of curiosity to abstractly calculate

large sets of permutations that would play out over many turns, it knew how to

win games by exploiting the limitations of the human brain. It is exactly this

combination of the ability to tap into a full repository of what has worked in past

games, explore strategies that are a bit out there, and then mercilessly

implement the ones best suited to the situation on the board that makes AI

algorithms unbeatable in most games.

DeepMind trained AlphaGo in a similar manner to how Amazon Web Services

trains its SageMaker machine learning platform to automatically apply the

optimal set of hyper-parameters for a specific model and use case.


https://www.nature.com/articles/nature24270.epdf?author_access_token=VJXbVjaSHxFoctQQ4p2k4tRgN0jAjWel9jnR3ZoTv0PVW4gB86EEpGqTRDtpIz-2rmo8-KG06gqVobU5NSCFeHILHcVFUeMsbvwS-lxjqQGg98faovwjxeTUgZAUMnRQ

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https://searchcio.techtarget.com/blog/TotalCIO/Google-applies-gamification-technique-to-neural-nets-and-optimizes-its-data-center

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First, the AI software takes a series of different guesses and observes the

predictive accuracy of each guess as the model training begins. It then

combines successful configurations from different models into one winning set

of hyper-parameters that can be expected to yield the best result for the specific

human-defined task.

The path from narrow AI to general uses

This type of algorithmic curiosity is similar to our oncologist knowing where to

start looking when evaluating a CT image for cancer. However, the auto-tuning

of hyper-parameters or AlphaGo's auto-detection of human weaknesses still

can't shake the brute force, iterative character of AI.

The AlphaGo developers made the correct assumption based on evolution

theory that some degree of variation is needed to come up with spectacular

wins. However, these guesses are initially random, without the human intuition -

- or bias -- accumulated through experience and training.

The cheaper that compute power becomes, the more this variation can be

introduced without too much cost impact, which could push AI past its lack of

intuition. But can all of this lead to general artificial intelligence?

Conceptually, if we assume unlimited compute power and humans preparing a

sufficiently large training set of text and multimedia data, a narrow AI application

could jump from playing chess to playing Go, and from there to taking orders at


https://searchenterpriseai.techtarget.com/news/252436163/Expert-panel-warns-developers-to-beware-of-AI-bias

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your local burger joint. However, the dependent variable is still missing for a lot

of potential AI use cases. And, in many cases, it has to be provided manually by

humans as part of the training process.

It doesn't even end there. How does a human train a self-driving car on whether

it's OK to run over an animal in a certain situation, or if it's better to apply the

emergency brake when doing so could mean getting rear-ended by an 18-

wheeler? The racist chatbot that Microsoft inadvertently deployed in 2016 is

only one of many examples where humans can provide a problematic frame of

reference for an AI tool.

These questions that extend beyond the purely technical aspects of AI will

continue to challenge the technology as it looks to extend beyond current

applications that are narrow in scope.


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New deep learning techniques take center stage

Maria Korolov, Technology Journalist

Generative adversarial networks, reinforcement learning and transfer learning

are approaches that have been explored by theoreticians and researchers for

years. Today, with recent improvements in technology, these deep learning

techniques are finally becoming practical for enterprise use.

"They are not really new concepts," said Hermann Ney, professor of computer

science at Germany's RWTH Aachen University and director of science at

speech recognition company AppTek. "But now, in the era of deep learning,

they have a better chance to be helpful."

GANs blur line between real and artificial

Last year, researchers from chipmaker Nvidia, based in Santa Clara, Calif.,

released a video showing computer-generated faces, cars and furniture suites

that were amazingly realistic.

The secret? Generative adversarial networks (GANs), in which two different AI

systems battle it out. One system tries to create realistic-looking images; the

other system tries to tell which ones are fake and which ones are real.


https://searchenterpriseai.techtarget.com/definition/deep-learning-deep-neural-network

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As the two duke it out, both get better and better, and the results can be

exceptionally lifelike -- and a little disturbing, said Vivek Katyal, global analytics

leader for risk and financial advisory at Deloitte.

"It's a pretty scary thing" how realistic these artificially created images can be,

he said.

But while GANs are of clear benefit to, say, film and video game companies

looking to fill out crowd scenes, there are other uses as well.

For example, companies can use it to take photographs and create 3D

renderings or even generate models for 3D printing.

"This is now being looked at in very advanced manufacturing," Katyal said.

There are also potential uses in other areas, he added, such as medical

imagery and generating the massive sets of training data that drive deep

learning. "The key application is generating image sets for learning data."

However, Katyal warned that companies need to be wary of inadvertently

introducing bias and errors into their systems. If an enterprise implements GANs

without first ensuring their data is clean, representative and unbiased, the deep

learning technique could magnify these problems.

"I don't see it inhibiting adoption," Katyal said. "But that's because I don't think

people today look at risk first. They look at what they can get out of it."


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Reinforcement learning creates new strategies

Last month, Google published the results of its experience with AlphaZero, a

system that learned to play Go and chess all by itself, without studying human

games or getting any feedback from people.

The secret was its use of reinforcement learning, one of the most cutting-edge

new deep learning techniques. The program played the games over and over in

an attempt to beat its own previous versions. It quickly evolved into a system

that could beat all existing competitors.

David Silver, who leads the reinforcement learning group for Google's

DeepMind, wrote in a recent blog post that this approach can lead to a more

creative kind of algorithmic game playing.

Reinforcement learning can be used by an AI system to teach itself how to do

almost anything, as long as there's a way to keep score.

Practical applications include navigation software that can enable robots to find

their way around new places that they don't have much data on yet or

manufacturing robots that learn how to interact with objects, said Jacob Perkins,

CTO at San Francisco security analytics company Insight Engines and author of

several books about machine learning software development.

"I don't know if Roomba uses reinforcement learning, but it would be a good

application of it," he said.


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In fact, any process that can be optimized could be a target for reinforcement

learning, said Christian Shelton, professor of computer science at University of

California, Riverside.

Optimization challenges, such as supply chain management, data center energy

use and cloud workload schedules, are currently being handled by other

approaches, such as traditional statistical methods that don't require

reinforcement learning. However, as these challenges get more complex

because, say, companies look at more contributing factors, reinforcement

learning will start coming into its own, Shelton said.

Transfer learning could lead to more natural AI

Transfer learning is something that comes naturally to humans. Once we learn

how to do one thing, we have an easier time learning a second related thing,

instead of having to learn each element of the second task from scratch.

This doesn't come naturally to computer programs, but AI programmers are

using various methods to give new systems a head start.

With the recent advancements of new deep learning techniques, the

possibilities of transferring knowledge have gotten better.

AppTek, for example, is a Virginia-based company that uses AI systems to

understand and translate spoken language.


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Transfer learning enables it to train its systems on large, publicly available data

sets, such as broadcast and entertainment videos and audio. Then, the learning

can be applied to other situations, such as user-generated videos or telephone

calls, where the sound quality is different, said AppTek CEO Mudar Yaghi.

"The results in all cases are more accurate predictions -- such as chatbots that

now recognize regional dialects or spellings," said Ken Sanford, analytics

architect and sales engineering lead at New York data platform company

Dataiku and professor at Boston College.

Sanford has worked with many companies on AI projects, including Walmart, in

his work for AI vendors and as an independent expert.

Walmart and other retailers are using transfer learning to help better categorize

products, he said. "They have too many new products, and the classification

system is too complex to do it manually." Transfer learning, in combination with

image recognition, can identify subtle differences among products.

Cloud providers that offer AI model-building services, including Google,

Microsoft and Amazon, are also using transfer learning.

"You upload a training set, they reference the history of all models they have

that appear close to it and they use the labeled training set to further hone the

model," Sanford said.


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New deep learning techniques may lead to more natural AI

Over the past decades, AI technology has improved dramatically, moving from

basic rules-based systems to statistical approaches. More recently, it's come to

be powered by the machine learning algorithms widely in use today, Deloitte's

Katyal said.

Now, GANs, reinforcement learning and transfer learning are helping take us

beyond machine learning and into narrow AI, he said. That's the next step on

the road to general AI.

"General AI is when it almost automates human intelligence," Katyal said.


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Researchers race for quantum AI as quantum computing advances

George Lawton, Contributor

Researchers have been exploring algorithms that would allow computers to

process data at the quantum level on a theoretical basis for many years, but

only now are the physical capabilities of quantum computers starting to catch up

to this theory. This could create opportunities for quantum AI that could allow for

the development of machine learning algorithms with less data.

It's still early, as existing quantum computers face several technical limitations

related to encoding quantum data, error-correction and the length of time that

calculations take. But researchers looking to create a more human-like type of

artificial intelligence may have to overcome these challenges. Some evidence

suggests that there is a quantum basis for human intelligence that complements

neural networks.

Targets of today's research

In the meantime, AI researchers will need to learn new approaches to building

quantum AI.


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"Regardless of task, the algorithms that run on quantum computers are

significantly different from those designed to run on classical computers," said

Bob Sutor, vice-president of quantum computing at IBM Research.

Sutor acknowledges there is still considerable work to be done in terms of

developing algorithms for AI within the constraints of today's approximate

quantum computing systems. But there has been some early research into

artificial neural networks run on the 5-qubit IBM Q Experience device published

by a team at the University of Pavia in Italy.

In the short run, quantum algorithm research could also inspire better AI on

classical computers.

"There have been examples of scientists discovering more-efficient ways to

solve problems in machine learning on classical computers due to what's been

learned about quantum algorithms," Sutor said.

For example, University of Washington doctoral student Ewin Tang developed a

better recommendation system following his research into quantum AI.

Early work on existing quantum computers also identified AI algorithms that

seem to work better than classical computers. For example, IBM worked with

Raytheon BBN in 2017 to perform certain black box machine learning tasks

more efficiently.



https://arxiv.org/abs/1807.04271


https://searchdatacenter.techtarget.com/feature/Dive-into-the-quantum-computing-qubit-with-IBMs-Bob-Sutor

http://www.nature.com/articles/s41534-017-0017-3

http://www.nature.com/articles/s41534-017-0017-3

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Quantum computing-inspired machine learning algorithms could also enable

developers to train models with less data or better understand the structures

and categories hidden within the data. Canadian quantum computing company

D-Wave has launched a machine learning business unit to help with this called

Quadrant.ai.

There are many directions for improving quantum AI algorithms, said Michael

Hartmann, associate professor at the Institute of Photonics and Quantum

Sciences School of Engineering and Physical Sciences at Heriot-Watt

University in Scotland. One line of research is looking at how to make the

individual calculation steps of machine learning algorithms faster. Another line

of research is exploring how quantum machine algorithms could operate on a

lower level of abstraction that is directly linked to the physical operations of the

quantum processor.

Dealing with errors

There are a variety of approaches being explored for building quantum

computers using different physical phenomena. But they share many common

challenges.

"In all efforts to build quantum information processing devices, keeping error

rates sufficiently low is the biggest challenge," said Hartmann.


https://quadrant.ai/

https://searchitoperations.techtarget.com/opinion/Machine-learning-algorithms-make-life-easier-until-they-dont

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Information stored in a quantum device is much more fragile than information in

classical computers.

Existing quantum computers work at very low temperatures so that the

materials used in the circuits restrict energy flow and prevent the basic units of

information -- qubits -- from changing state. The electrical circuits used in

classical computers at room temperature would destroy all quantum information.

Quantum computing depends on maintaining coherence across the qubit

computing elements in a quantum system. Qubits are the quantum equivalent of

a bit, but it can be used to encode significantly more information than a bit. They

are also more prone to errors. Today's quantum computers are considered

approximate systems. They have errors and short coherence times within which

to run algorithms.

"There are physical device limitations we still need to overcome before we have

fault-tolerant universal quantum computers that operate with the stability we

expect from classical computers," IBM's Sutor said.

As it turns out, some quantum AI algorithms are less impacted by errors.

Hartmann said the Quantum Approximate Optimization Algorithm is a strong

candidate to run on near-future quantum computers, as it does not require

quantum error correction, which have a large resource overhead.


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Another big challenge lies in encoding the data into quantum memory systems

in a way that maintains its quantum state. However, loading classical data into a

quantum memory is demanding.

"If you want to exploit the ability of a quantum computer to handle much more

data than a classical one, you still need to convert the classical data into

quantum data at the beginning," Hartmann said.

This can be the dominant effort when building a practical algorithm. And this

process must be repeated for each new machine learning application because

researchers have not found a way to store quantum state data for very long.

Hope for human-like AI

A popular idea suggests that we are on the verge of creating classical

computers with more processing power than humans possess, which could lead

to sentient machines, or the singularity.

But researchers like Roger Penrose and Stuart Hameroff think this is off base.

In the mid-90s, they postulated that there may be a quantum basis to human

intelligence with their Orchestrated Objective Reduction theory. The implication

of the theory is that AI will have to move beyond classical computing models if it

is going to replicate human-like intelligence.

Late last year, Pavlo Mikheenko, associate professor of condensed matter

physics at the University of Oslo, found some physical evidence that quantum


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strings exist in human brains. He noted that this work is still early, and he is

waiting for others to confirm his findings.

"My research directly suggests that there is a quantum aspect to biological

intelligence," he said. "The brain seems to be both superconducting and

quantum."

The one big challenge in replicating the quantum information processing of the

brain in quantum computers is that information processing structures in the

brain appear to be highly dynamic and seem to break down after a few minutes

only to be recreated within the cells. In contrast, existing approaches to building

quantum computers are designed to work with stable quantum structures.

"It would be helpful if the relevant quantum computing technology was the same

as the memory storage rather than having to search and introduce memory,

Hameroff said. "Quantum processing relevant to consciousness runs on the

same structures encoding memory."

Getting some practice

It may be a while before quantum hardware catches up with the theory. But in

the meantime, developers can learn some of the basic principles by tinkering

with quantum computing cloud services like IBM's Qiskit and D-Wave' Leap

Quantum Application Environment.


https://searchcio.techtarget.com/feature/Barclays-Bank-takes-a-crack-at-IBMs-quantum-computer

https://developer.ibm.com/code/open/projects/qiskit/

http://www.dwavesys.com/leap

http://www.dwavesys.com/leap

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"AI is for many developers, on the top of the agenda for the first applications of

quantum computers that are currently being developed," said Hartmann.

Existing quantum computers only demonstrate a proof of principle, but Hartman

sees many companies pushing hard to achieve the first demonstration of

quantum computing power that exceeds the capabilities of classical AI.

"I expect the demand for it to be huge once it is there," Hartmann said.


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Augmented intelligence: The clearest path to focused AI?

David Petersson, Freelance Writer

Companies are competing in a billion dollar arms race to implement artificial

intelligence, but experts are beginning to see cracks in the relentless pursuit.

This is primarily because, so far, big AI promises have produced little concrete

innovation. Most AI technology still struggles to complete tasks that a four-year-

old child accomplishes effortlessly.

The current "intelligent" systems are shortcuts that detect patterns based on

statistical methods but still have no understanding of what they have detected.

This understanding and the ability to mimic human task completion -- what

developers refer to as artificial general intelligence -- is a long way from full

development.

Rather than focus on a general goal to improve artificial intelligence, CIOs and

businesses should start paying attention to -- and investing in -- augmented

intelligence technology. Augmented intelligence technology is a form of artificial

intelligence that does not seek to replace humans, but instead seeks to assist

humans with their work. This makes augmented intelligence a concrete and

ROI-accessible alternative to AI.


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https://whatis.techtarget.com/definition/augmented-intelligence

https://searchenterpriseai.techtarget.com/feature/Is-artificial-general-intelligence-possible-in-our-lifetime


https://searchcio.techtarget.com/news/252450238/Augmented-human-intelligence-Using-AI-to-streamline-business-process

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No replacement for humans

The automotive industry is a great example of the potential of AI, but also

serves as a great example of AI's flaws. Mercedes, Toyota and Nissan were

among the companies that touted their self-driving car concepts, but the biggest

question to come from the 2018 Consumer Electronics Show in Las Vegas was,

"Where are the autonomous vehicles?"

We need to find the correct balance and, for now, it seems the best way would be to

combine AI with human intelligence and form augmented intelligence technology.

Tesla's Autopilot is not a full replacement for self-driving, and Uber's vision was

crushed after a fatal accident involving one of its self-driving cars. Tesla's

Autopilot, on the other hand, recently crashed into a stopped firetruck because

the AI is trained to ignore them. Trying to lean toward the side of safety,

Waymo's cars try to abide every single traffic rule but create inferior systems

that would fail basic driving tasks.

Augmented intelligence overrides the critical problem of AI adoption: that the

systems are reactive, not proactive. A human driver has the ability to make

educated guesses about what will happen on the road 10 seconds in the future,

whereas the current AI systems have no way to make such predictions. It's here

that augmented intelligence could make a difference. The trick is to train the

system so that it accurately recognizes the subjects it is trained on, as well as

offer reasonable performance when it comes to unforeseen road hazards.


https://www.theverge.com/transportation/2018/1/11/16873102/mercedes-benz-smart-vision-eq-concept-car-ride-ces-2018

https://www.theverge.com/2018/1/8/16863092/toyota-e-palette-self-driving-car-ev-ces-2018

https://www.theverge.com/2018/1/15/16892570/nissan-xmotion-suv-screen-detroit-auto-show-2018

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https://www.wired.com/story/tesla-autopilot-why-crash-radar/

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An extra 'Eye'

Discoperi Inc.'s System 'Eye' is reflective of augmented technology as a step to

complete AI adoption. The Eye is a camera placed on a vehicle that combines

the data from several cars that allows the system to form a grid to assist all

nearby drivers. This augmented intelligence doesn't operate the vehicle, but

instead assists the primary, human operator by detecting objects and behavior

patterns to recognize when a driver is breaking from normal patterns and driving

dangerously -- hoping to reduce fatal accidents.

The Eye demonstrated great accuracy when detecting objects on the road, but

the AI's primary task is to build behavior patterns. Of course, there are several

parameters involved, such as where an event happens, under what conditions

and whether there are pedestrians on the road. The system checks what's

normal under these circumstances to what is currently happening, and if it is

beyond a certain threshold, it will send an alert to all cars within that proximity.

While access to information about every car on the road sounds like a privacy

nightmare, Discoperi has already taken steps to ensure privacy as well as give

users full control over their data by storing the data on a blockchain.

Privacy might seem more like a problem for augmented intelligence because it

involves human input, while artificial intelligence is theoretically fully

autonomous. But due to the real shortcomings in AI, many companies have


https://www.newswire.com/news/system-eye-by-discoperi-can-save-300-000-lives-per-year-20565091

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already used humans behind the scenes to complete AI's job where it failed,

raising privacy concerns in AI.

Universal implementation

Augmented intelligence capabilities will benefit industries -- perhaps even more

than traditional AI would. In healthcare, an AI algorithm can analyze a patient's

symptoms and vital signs, compare it with the history of the patient, their family

and millions of other patients, and provide possible diagnoses a for a doctor to

analyze. In education, AI can track the progress of the students and help

teachers understand what topics and which students need more attention.

Augmented intelligence can contribute to streamlining business processes and

aid human workers in a concrete, helpful and cost-effective manner.

AI has undergone two winters by now, and the next one is looming. But this

time, AI has made far too much progress to be forgotten again. CIOs can't

continue investing in stalled technology, and they should also resist giving up

the benefits of AI technology. We need to find the correct balance and, for now,

it seems the best way would be to combine AI with human intelligence and form

augmented intelligence technology.


https://www.theguardian.com/technology/2018/jul/06/artificial-intelligence-ai-humans-bots-tech-companies

https://searchenterpriseai.techtarget.com/feature/Augmented-intelligence-applications-showing-ROI-broad-success

https://www.computerweekly.com/news/252448295/Why-AI-should-assist-humans-not-replace-them

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Limits of AI today push general-purpose tools to the horizon

Torsten Volk, Managing Research Director

When we watched Star Trek and 2001: A Space Odyssey, we developed a very

specific idea of artificial intelligence as a humanlike, or even superhuman, entity

that can live in starships or computers and answer any question in real time

based on humanity's collective knowledge.

Today, AI is the most talked-about topic in enterprise IT, with all major

enterprise software vendors aggressively promoting their stories. However, the

limits of AI today make the technology very different from what the movies

taught us to expect. This does not mean that AI today cannot provide ROI. But

we need to continue pushing toward the next frontier of a more general AI

approach that develops universal problem-solving capabilities with minimal

supervision and significantly reduced training requirements.

What AI should be

If AI and machine learning are everywhere, why do employees still get bogged

down with so many tedious, manual tasks? Why can't an application on my

laptop look at my calendar invites and book flights, hotels and rental cars based





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on my schedule? Why can't it remind me of conferences that are relevant to my

field? Of course, it should also monitor my credit card bill, Uber account and

airline and hotel charges to automatically create my expense report. And when I

write a paper or prepare for a presentation, it should automatically surface

relevant contextual info based on the audience, my personal preferences and

current industry trends and events.

The difference between the AI scenario described above and today's reality is

rooted in the fact that neural networks and reinforcement learning models

require too much manual architectural training and deployment effort. Neural

networks are best at solving problems through reliance on massive number-

crunching without much contextual awareness.

AI tools that perform a broad range of tasks, like object and facial recognition,

speech-to-text transcription and optimization of logical unit numbers on a

storage array, all have one thing in common: They use a large amount of

processing power to analyze hundreds of thousands of training data points to

identify subtle correlations between many features. However, each one of these

AI models needs to be trained, configured and architected to be used for a

single task, which is one of the major limits of AI today.

When asked whether a given photo was taken on Mercury or on Earth, for

example, a human will most likely know that there is no camera located on

Mercury, so he or she can infer that the photo can only have been made on

Earth. An untrained neural network will simply find no matches of Earth or


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Mercury landscapes and not return a result. It cannot generalize common

principles and concepts but exclusively relies on the patterns it has identified

within each training set. While an untrained human can figure out how to solve

many advanced tasks and challenges without any specific training, the limits of

AI mean it does not have this ability.

Delayed rewards as the key to unlock a strategic AI

Google's AlphaGo managed to beat the world's best Go players only after the

AlphaGo project team created an algorithm that goes beyond simply optimizing

its next move. AlphaGo learned to play sophisticated strategies that led to

reliable wins over human opponents by gradually identifying human players'

weaknesses and by playing thousands of simulated games against itself. The

reinforcement algorithm encouraged AlphaGo to make unexplored moves.

When these moves led to a loss, AlphaGo would explore different moves,

always rewarding game wins and avoiding moves that were similar to the losing

ones.

AlphaGo's reward algorithm could be seen as a starting point. But instead of

merely rewarding positive behaviors and penalizing negative ones, future, more

general AI applications need to explore a much more constructive approach for

complex problem-solving. This approach could be called "strategic curiosity,"

and it is inspired by the concept of the Memory, Attention and Composition

(MAC) network -- pioneered by Stanford researchers Christopher Manning and

Drew Hudson. The term strategic curiosity refers to the idea of training an AI to


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make educated guesses based on what it knows about the environmental

context that is relevant to its specific task.

The core idea of Manning's MAC network is to provide the learning network with

a knowledge base that it can use when working on answering a specific user

question, such as: Which planet is second closest to the sun and has a ring

around it?

The network trains on a few thousand natural language questions and answers,

such as: Which planets are blue? Is Venus further from Earth than Jupiter?

What is the third largest planet? Traditional neural networks, by comparison,

require hundreds of thousands, or even millions, of examples in training data

sets, one of the major limits of AI today.

Based on this training and a knowledge base -- in this case, the planet map

below -- the MAC network can respond to our natural language question with a

much higher accuracy than traditional neural networks.

Here are the stages of the model's decision process:

1. The model looks at the first part of the question -- second closest to the sun -- and focuses its attention on Venus.

2. The model looks at the second part of the question -- planets with rings -- and sees that Uranus comes after Saturn among planets with rings.



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Why is this simple example so interesting? Because the MAC network model is

able to answer each part of a question by memorizing relevant content

knowledge it gained from understanding the individual components of that

question. In our example, the MAC network keeps the fact that it knows that we

are looking for something that is the second closest to the sun in memory. Then,

when the next MAC cell responds to the second part of the question -- "has a

ring around it" -- it adjusts its answer based on contextual knowledge it

previously learned.

Bringing back the excitement to AI

Manning's MAC network, while still in its adolescence, shows the potential of

bringing AI to the next level by modeling the ability of our brain to learn from

context. When asked questions, like "Which color are this tree's leaves?" the

human brain will instantly make our eyes look up into the tree, instead of

scanning our entire field of view. Even more, our brain would already know from

context that it is summer and all the trees on our street have green leaves. Of

course, the MAC network is still far away from this type of reasoning, but it is

significant, as this type of breakthrough keeps the AI excitement going strong.


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Humans and AI tools go hand in hand in analytics applications

Craig Stedman, Editor at Large

AI tools may be intelligent, but they aren't all-knowing -- and they can learn a

thing or two from people.

That's the view of analytics and engineering managers whose teams apply a

human touch to the work of machine learning algorithms and other forms of AI.

Pairing up humans and AI software provides information that the technology

can't deliver on its own -- and it prevents organisations from blindly following

algorithms down the wrong business paths.

Referred to by proponents as human in the loop, the idea is to tap

knowledgeable data analysts or business users to give feedback on the findings

of AI tools, particularly in cases where there's uncertainty about the validity of

what they find. The resulting feedback loop supports so-called active learning

approaches designed to eliminate errors or fill in missing info, and can then train

algorithms to produce better results in the future.

For example, O'Reilly Media Inc. uses a combination of humans and AI to label

and categorize the content of the videos recorded at the technology


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conferences it runs. Data analysts can't handle that task themselves, according

to Paco Nathan, director of the company's online learning unit. In a presentation

at this month's Strata Data Conference in San Jose, Calif., Nathan noted that

O'Reilly was recording about 200 hours of video there -- and the event was just

one of the 20 or so conferences it puts on each year.

Word games lead to AI uncertainty

However, the natural language processing (NLP) algorithms that the

Sebastopol, Calif., company uses to parse the video content often get confused

by words with multiple meanings or ambiguous contexts, Nathan said. When

such cases are identified, a person from his team must step in to figure out the

correct meaning and to label the content accurately.

The output of the NLP-driven machine learning models is stored as a log file in

a Jupyter Notebook, which the analysts can review and update. "It's really a

two-way street, and you end up with documents that are collaborative, partly

done by machines and partly done by people," Nathan said.

He added that O'Reilly, which jointly organizes the Strata conference with big

data vendor Cloudera, is seeing more than 90% accuracy with content labeling

due to the combined AI and human efforts.

Pinterest Inc. uses a group of machine learning applications to drive the

operations of its image search and bookmarking website, including the search


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process and things like ad placement and content labeling. But the San

Francisco-based company relies on human evaluators to check what the

algorithms produce for relevance and accuracy.

They also take part in A/B testing of algorithm-generated user interface designs,

rating the different options based on personal preference, said Veronica Mapes,

a technical program manager at Pinterest who is in charge of the human

evaluation effort.

After initially doing all of the evaluation work on third-party crowdsourcing

platforms, Pinterest built its own human evaluation system in 2016, and it used

that to significantly expand the rating efforts last year, according to Mapes. The

company still uses outside platforms, too, but it now has full-time employees

and internal contractors involved in the process, a step designed to both reduce

costs and improve the quality of the evaluations, Mapes said in a Strata

session.

The intermingling of humans and AI applications is part of a broader effort

pushed by Pinterest executives to ensure that the website provides useful info

to visitors, said Garner Chung, engineering manager for the Pinterest human

evaluation team. The human input acts as a counterbalance to standard

engagement metrics that track what users click on and how much time they

spend on pages, he said.


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Engagement is the signal that the machine learning algorithms are based on,

"but that's not always the best metric to use," Chung said after the session. "We

really don't just want to be serving up clickbait and turning our users into

zombies."

Chung cited the example of links to content on lowering body fat showing up in

the results of a search for chicken recipes. That might seem like a logical

connection to an algorithm, but it's one that a human evaluator should flag as

not directly relevant to the search, he said.

AI's limits leave humans in the loop

In general, AI software isn't close to being ready to fully take over the work that

people do, said Michael Chui, a partner at the McKinsey Global Institute who

leads research on how technology innovation affects businesses and society.

"There are real limitations to AI now," Chui said at the Strata conference. "Don't

think these technologies can do everything."

Ebates Inc., which runs a shopping rewards program for consumers, is in the

early stages of AI adoption. The San Francisco-based company uses semi-

autonomous machine learning models to rank member preferences to better

target cash-back offers and to help detect odd buying behavior or other

potentially fraudulent activities, said Mark Stange-Treager, its vice president of

analytics. In the next 12 to 18 months, he expects to start running full-fledged AI


https://searchenterpriseai.techtarget.com/news/450422957/AI-taking-jobs-isnt-the-problem-how-it-will-change-them-is

https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/artificial-intelligence-in-business-separating-the-real-from-the-hype

https://searchenterpriseai.techtarget.com/news/450423702/AI-analytics-expected-to-rise-along-with-management-complexity

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algorithms against the company's Hadoop data lake, which has AtScale's data

management platform layered on top.

Even then, though, Ebates will likely continue to combine the work of humans

and AI in analytics applications, Stange-Treager said in a post-Strata interview.

For example, he said he sees a continuing need for manual reviews by workers

at the company to determine whether member behavior flagged as anomalous

by an algorithm is problematic.

"I envision a scenario where we're using algorithms to do a lot of the work, but

not just letting them go off and do their own thing," Stange-Treager said. "I'm not

saying we won't get there in the future, but I think that's well off in the future

from our point of view."


https://searchbusinessanalytics.techtarget.com/news/252466854/Updated-AtScale-platform-streamlines-analysis-of-big-data

https://searchbusinessanalytics.techtarget.com/news/252466854/Updated-AtScale-platform-streamlines-analysis-of-big-data

https://searchbusinessanalytics.techtarget.com/essentialguide/Guide-to-using-advanced-analytics-and-AI-in-business-applications

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Panel: What we call AI today doesn't live up to hype

Ed Burns, Executive Editor

Not everyone believes that the technology we call artificial intelligence today

lives up to the hype it's generated in the last year, and the gap between reality

and hype could influence how the technology is ultimately used by enterprises.

"We still don't have real AI because we still don't know how the brain and mind

work," MIT professor Josh Tenenbaum said in a panel discussion at MIT's

Sloan CIO Symposium.

The panel discussed the differences between the type of applications we're

calling AI today and true AI, programs that can think and learn for themselves.

In general, the participants saw a wide gulf between the current state of the art

and the ideal of true AI.

"The one caution I'd bring forward is to set expectations correctly," said Ryan

Gariepy, co-founder and CTO at Clearpath Robotics Inc. "When you're working

in your organisation and exploring this technology, we've seen examples in the

past where these expectations get so high and people starting buying the

technology and then nothing happens."


https://searchenterpriseai.techtarget.com/feature/Tech-experts-weigh-in-on-the-AI-hype-cycle

https://searchenterpriseai.techtarget.com/feature/Tech-experts-weigh-in-on-the-AI-hype-cycle


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AI has promise, but keep it in perspective

Gariepy said there is no doubt that the systems we call AI today are a vast

improvement on the AI technology of just a few years ago. Clearpath makes

autonomous vehicles for industrial applications like mining and warehousing.

These drones couldn't function without the kind of machine learning and

computer vision that today are lumped into the general AI category, according to

Gariepy.

But despite this kind of progress, we're still a long way from truly autonomous

robots that can think and function on their own without any kind of human

supervision, Gariepy said.

"That's something we need to be careful about," he said. "There's a tremendous

amount of potential in AI, but let's not say it's going to solve every problem

without human intervention."

The point here is not just about defining terms. Whether the AI we see today is

true intelligence or something short of that plays into how applications are used.

After all, the existence of truly autonomous, intelligent systems could pave the

way toward full job automation of everything from rote, routine tasks to higher-

level knowledge work.

The notion that AI could automate all of our jobs has sparked debate about far-

ranging topics such as political stability and the possible need for universal


https://searchenterpriseai.techtarget.com/definition/driverless-car

https://searchenterpriseai.techtarget.com/quiz/Quiz-Find-out-how-smart-you-are-about-machine-learning-and-AI


https://www.computerweekly.com/news/4500267840/Emotional-intelligence-will-save-us-from-job-automation

https://www.computerweekly.com/news/450415491/AI-and-robots-will-create-political-instability-until-humans-find-new-occupations

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incomes. But panelists said people are ahead of themselves when they get into

these topics because today's AI technology is not ready to put that many people

out of work.

AI won't automate all jobs

Today's technology is far more likely to augment workers rather than automate

their jobs.

"Since the very beginning of AI there's always been the debate about

augmentation versus automation and that's very much still happening today,"

MIT professor Joi Ito said. "I don't think automation is an optimal answer."

Instead, he and other panelists said they believe AI will fill in for workers on the

most routine tasks that demand simple pattern recognition and other basic

skills. In their view, this will remove a lot of the drudge work from jobs and allow

human workers to focus on the more creative and interesting aspects of their

jobs.

But it's still early to even talk about this. The platforms we call AI today are

finding the most success in fairly simple applications, like call centers and other

customer service venues. Thinking about AI-assisted workflows for other types

of jobs in areas like law, healthcare and journalism are hard because the

technology is still so new and functionality is relatively limited, panelists said.


https://searchcustomerexperience.techtarget.com/report/Leveraging-speech-recognition-technology-in-call-centers

http://www.mitcio.com/panel/putting-ai-to-work/

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"The challenge any organisation faces today is, how do you get there?" said

Seth Earley, CEO at consulting group Earley Information Science Inc. "There's

this vision of the future where everything is going to change. How do you get

from here to there? You have to look for processes to automate but still keep

people engaged."


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