Artificial Intelligence Primer

It will either be the best thing that’s ever happened to us, or it will be the worst thing. If we’re not careful, it very well may be the last thing.

Stephen Hawking

What is Artificial Intelligence?

Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry. For example, speech recognition, problem-solving, learning and planning.

Today, Artificial Intelligence is a very popular subject that is widely discussed in the technology and business circles. Many experts and industry analysts argue that AI or machine learning is the future – but if we look around, we are convinced that it is not the future – it is the present.

With the advancement in technology, we are already connected to AI in one way or the other – whether it is Siri, Watson, or Alexa. Yes, the technology is in its initial phase and more and more companies are investing resources in machine learning, indicating a robust growth in AI products and apps in the near future.

Less than a decade after breaking the Nazi encryption machine Enigma and helping the Allied Forces win World War II, mathematician Alan Turing changed history a second time with a simple question: “Can machines think?” 

Turing’s paper “Computing Machinery and Intelligence” (1950), and it’s subsequent Turing Test, established the fundamental goal and vision of artificial intelligence.   

At it’s core, AI is the branch of computer science that aims to answer Turing’s question in the affirmative. It is the endeavor to replicate or simulate human intelligence in machines.

The expansive goal of artificial intelligence has given rise to many questions and debates. So much so, that no singular definition of the field is universally accepted.  

The major limitation in defining AI as simply “building machines that are intelligent” is that it does not actually explain what artificial intelligence is? What makes a machine intelligent?

In their groundbreaking textbook Artificial Intelligence: A Modern Approach, authors Stuart Russell and Peter Norvig approach the question by unifying their work around the theme of intelligent agents in machines. With this in mind, AI is “the study of agents that receive precepts from the environment and perform actions.” (Russel and Norvig)

Norvig and Russell go on to explore four different approaches that have historically defined the field of AI: 

1. Thinking humanly
2. Thinking rationally
3. Acting humanly 
4. Acting rationally

The first two ideas concern thought processes and reasoning, while the others deal with behavior. Norvig and Russell focus particularly on rational agents that act to achieve the best outcome, noting “all the skills needed for the Turing Test also allow an agent to act rationally.” (Russel and Norvig).

What are Neural Networks?

Neural nets are a means of doing machine learning, in which a computer learns to perform some task by analyzing training examples. Usually, the examples have been hand-labeled in advance. An object recognition system, for instance, might be fed thousands of labeled images of cars, houses, coffee cups, and so on, and it would find visual patterns in the images that consistently correlate with particular labels.

Modeled loosely on the human brain, a neural net consists of thousands or even millions of simple processing nodes that are densely interconnected. Most of today’s neural nets are organized into layers of nodes, and they are “feed-forward,” meaning that data moves through them in only one direction. An individual node might be connected to several nodes in the layer beneath it, from which it receives data, and several nodes in the layer above it, to which it sends data.

The recent resurgence in neural networks – the deep-learning revolution – comes courtesy of the computer-game industry. The complex imagery and rapid pace of today’s video games require hardware that can keep up, and the result has been the graphics processing unit (GPU), which packs thousands of relatively simple processing cores on a single chip. It didn’t take long for researchers to realize that the architecture of a GPU is remarkably like that of a neural net.

Modern GPUs enabled the one-layer networks of the 1960s and the two- to three-layer networks of the 1980s to blossom into the 10-, 15-, even 50-layer networks of today. That’s what the “deep” in “deep learning” refers to – the depth of the network’s layers. And currently, deep learning is responsible for the best-performing systems in almost every area of artificial-intelligence research.

What is Machine Learning?

Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process. As the algorithms ingest training data, it is then possible to produce more precise models based on that data.

A machine-learning model is the output generated when you train your machine-learning algorithm with data. After training, when you provide a model with an input, you will be given an output. For example, a predictive algorithm will create a predictive model. Then, when you provide the predictive model with data, you will receive a prediction based on the data that trained the model.

Iterative learning

Machine learning enables models to train on data sets before being deployed. Some machine- learning models are online and continuous. This iterative process of online models leads to an improvement in the types of associations made between data elements. Due to their complexity and size, these patterns and associations could have easily been overlooked by human observation.

After a model has been trained, it can be used in real time to learn from data. The improvements in accuracy are a result of the training process and automation that are part of machine learning.

Approaches to machine learning

Machine-learning techniques are required to improve the accuracy of predictive models. Depending on the nature of the business problem being addressed, there are different approaches based on the type and volume of the data.

What is Deep Learning?

Deep learning has been a challenge to define for many because it has changed forms slowly over the past decade. To set deep learning in context visually, the figure below illustrates the conception of the relationship between AI, machine learning, and deep learning.

Deep learning is a subset of machine learning, whose capabilities differ in several key respects from traditional shallow machine learning, allowing computers to solve a host of complex problems that couldn’t otherwise be tackled.

Deep learning then can be defined as neural networks with a large number of parameters and layers in one of four fundamental network architectures:

• Unsupervised Pre-trained Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• Recursive Neural Networks

Deep learning has solved many problems that were previously believed to be off-limits for computers. But the achievements of deep learning have led to many wrong interpretations and expectations of its capabilities. While being a very exciting technology, deep learning also has distinct limits. For example, some of these limitations include:

• Deep learning requires a lot of data. Unlike humans, who can learn concepts and make reliable decisions based on limited and incomplete data, deep learning models are often only as good as the quality and quantity of data they’re trained with. This poses a limit in areas where annotated data is not available.
• Deep learning models are shallow: Deep learning and neural networks are very limited in their capabilities to apply their knowledge in areas outside their training, and they can fail in spectacular and dangerous ways when used outside the narrow domain they’ve been trained for.
• Deep learning is opaque: Unlike other machine learning models, deep learning involves very little top-down human design. They are also very complicated and involve thousands and millions of parameters. This makes it hard to interpret their outputs and the reasoning behind their decisions. Neural networks are described as black boxes because of their opacity. The problem has given rise to a series of efforts and studies toward creating explainable AI.

Deep learning and neural networks are often compared with human intelligence. But while deep learning can perform some complicated tasks on par or better than humans, it works in a way that is fundamentally different from the human mind. It is especially limited in commonsense and abstract decision-making.

What is needed for Artificial Intelligence to work?

Natural Language Generation

Even for humans to communicate efficiently and clearly can be tricky. Similarly, for machines to process information is an entirely different process than the human brain, and it can be extremely tricky and complex. Natural Language Generation is a sub discipline of AI that converts text into data and helps the systems to communicate ideas and thoughts as clearly as possible. It is used in customer service, widely, to create reports and market summaries. Companies like Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS and Yseop offer Natural Language Generation.

Speech Recognition

Speech Recognition is used to convert and transform human speech into a useful and comprehensive format for computer applications to process. The transcription and transformation of human language into useful formats is witnessed often nowadays and is growing rapidly. Companies like NICE, Nuance Communications, OpenText and Verint Systems offer speech recognition services. 

Machine Learning Platforms

Machine Learning is a sub discipline of computer science as well as an important branch of Artificial Intelligence. Its objective is to develop new techniques enabling computers to learn and hence become more intelligent. With the help of algorithms, APIs (application programming interface), development, training tools, big data and applications, machine learning platforms are becoming more popular. They are widely used for the purpose of categorization and prediction. Amazon, Fractal Analytics, Google, H2O AI, Microsoft, SAS, Skytree and Adtext are some of the companies selling machine learning platforms. 

Virtual Agents

A virtual agent refers to a computer agent or a program that is capable of interacting effectively with humans. Virtual agents are trending artificial intelligence technologies, currently, it is used in customer service through Chatbots as well as a smart home manager. Companies that provide virtual agents are Apple, Google, Amazon, Artificial Solutions, Assist AI, Creative Virtual, IBM, IPsoft, Microsoft and Satisfi. 

Decision Management

Artificially Intelligent machines have the capability of introducing logic to AI systems in order to gear them up to be used for training, maintenance and tuning. In order to add value to the business and profitable, decision management is already being used by organizations by incorporating it into their applications to propel and execute automated decision. Some companies that provide this service are Informatica, Advanced Systems Concepts, Maama, Pega Systems, and UiPath.

AI Optimized Hardware

Owing to better and improved graphics as well as central processing units, devices are being structured and used to execute AI oriented tasks specifically. A prominent example of this is the AI optimized silicon chip which can be inserted into any portable device. Therefore, companies and organizations are investing greatly in AI to accelerate the next generation of applications. This technological service is offered by companies like Alluviate, Google, Cray, Intel, IB and Nvidia. 

Deep Learning Platforms

Deep Learning Platforms is a form of machine Learning that duplicates the neural circuits of the human brain to process data and create patterns for decision making. In this unique technology, algorithms use artificial neural networks. A few of its applications are automated speech recognition, image recognition and prediction of anything that can be sensed in the digital sphere. Deep learning platform providers are Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion, Saffron Technology, Sentient Technologies and Leverton. 

Robotic Process Automation (RPA)

Robotic Process Automation, often simply called to as RPA, refers to the functioning of corporate processes due to the mimicking human tasks and automate them. In this particular sphere, it is important to bear in mind that AI is not meant to replace humans, but to support and complement their skills and talent. Companies like Pega systems, Automation Anywhere, Blue Prism, UiPath and WorkFusion focus on this process. 

Text Analytics and Natural Language Processing (NLP)

Natural Language Processing focuses on the interactions between human languages and computers. It uses text analytics to analyze the structure of sentences as well as their interpretation and intention through machine learning. This technology is widely adopted in fraud detection and for security systems. Many automated assistants and applications derive unstructured data by NLP. Basis Technology, Expert System, Coveo, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd and Synapsify are some of the service providers.

Bio-metrics

Bioetrics deals with the recognition, measurement and analysis of the physical features of the body’s structure, form and human behavior. It fosters organic interactions between machines and humans as it works with touch, image, speech and body language. It is predominantly used for the purpose of market research. #VR, Affectiva, Agnitio, FaceFirst, Sensory, Synqera and Tahzoo provide this technology service.

Cyber Defense

Cyber Defense is a computer defense mechanism that aims to detect, prevent and mitigate attacks and threats to data and infrastructure of systems. Neural networks that are capable of processing sequences of inputs can be put to use along with machine learning techniques to create learning technologies in order to reveal suspicious user activity and detect cyber threats.

Content Creation

Although content is created by people working on videos, ads, blogs and white papers; brands like Hearst, USA Today ad CBS are using AI to generate  content. Wordsmith is a popular tool created by Automated Insights, which applies NLP in order to generate news stories.

Emotion Recognition

This kind of AI technology enables emotions expressed by humans to be read and interpreted using advanced image processing or audio data processing. Law enforcers often use thus technology during interrogation. Some companies who use emotion recognition are Beyond Verbal, nViso and Affectiva.

Image Recognition

Image recognition refers to the process of identifying and detecting a feature in a video or an image. It can help the process of image searches greatly as well as detect license plates, diagnose diseases and study personalities. Clarifai, SenseTime and GuGum’s provide this technology service.

Marketing Automation

Marketing and sales teams and divisions have adopted AI and benefited a lot from it in return. Methods incorporating AI through automated customer segmentation, customer data integration and campaign management are widely used. AdextAI has grown to become a pioneer in adopting marketing automation.

Applications

Healthcare

Artificial intelligence (AI) has the potential of detecting significant interactions in a dataset and also it is widely used in several clinical conditions to expect the results, treat, and diagnose. Artificial intelligence (AI) is being used or trialed for a variety of healthcare and research purposes, including detection of disease, management of chronic conditions, delivery of health services, and drug discovery.

Social Media

We live in an era where information is just a click away. We are influenced by it and want to share our thoughts about it. This is where social media enters. Social media is a part of our day-to-day life that can’t be ignored. It has both positive and negative effects.

Most of us are addicted to many social media platforms like Facebook, Twitter, Instagram, etc. and it is considered odd not to be connected. Social media has grown to become a large platform for entrepreneurs, businesses, organizations and various other professionals who seek identification and recognition at a moderate cost.

In order to succeed, businesses and organizations opt for Artificial Intelligence which is becoming more and more common in today’s world and social media seems to be doing the same.

With the growing technology, a wide variety of tools are being used in order to target the right audience on social media. And AI is creating a better journey for users by developing a better user experience on social platforms. Make no mistake, all social media platforms are using AI to manipulate the users and feed on this addiction for more.

eCommerce

Amazon is the 800-pound gorilla is every brand’s c-suite. Whether it’s their usability, recommendations, or membership perks, Amazon wins because they do customer service right – and at scale. What many businesses don’t know, though, is that the secret to Amazon customer service success doesn’t have to mean an Amazon business size.

Amazon made AI its competitive advantage. And don’t expect them to stop pushing the limits on the power of data and AI. And you can expect even more innovative from Amazon, especially in the AI arena. Why? Because Amazon’s patent on one-click payments is set to expire this year. Losing the one feature that led the giant to domination won’t deter them. It will only feed the fire to find new ways to be disruptive.

Digital Assistant

Apple’s Siri, Amazon’s Alexa, the Google Assistant, and Microsoft Cortana are all advanced AI driven Digital Assistants. AI-powered digital assistants are software programs. They might use specific hardware like a smart speaker. Alternatively, you might find them as a feature on your smartphone, laptop, or a wearable device.

These digital assistants take directions or requests from the user. Subsequently, they find information and perform a task. Earlier software programs used rules-based automation to perform tasks for their users. AI-powered digital assistants work very differently though.

The limitations of the technology: A lot of research and developments are still going on as far as AI is concerned. One can categorize the current crop of AI technologies used in these digital assistants as “Weak AI”. They incorporate a limited amount of intelligence. We are yet to see “Strong AI” that employs a higher degree of intelligence, and we will not see that anytime soon. As a result, AI digital assistants can only handle relatively simpler tasks.

Privacy concerns: When you keep your device with an AI digital assistant on, it can potentially record a lot of your conversations. There have been reports that human employees of the technology giants listen to the recorded audio clips that contain users’ requests to their digital assistants. To make matters more complex, the technology giants can access your location data along with the recorded audio clips. All of these combines to create serious privacy concerns among many users of AI digital assistants. Many Internet users do not trust technology giants like Google, Amazon, Apple, and Microsoft to keep their data private, which compounds the problem.

Security concerns: All your requests to Siri, Alexa, Google Assistant, and Microsoft Cortana are stored on the cloud. There have been many reports of hackers successfully attacking cloud infrastructure and stealing sensitive data. As a result, many users of these digital assistants have security concerns around their data.

Weather

Weather forecasting is notoriously difficult, but in recent years experts have suggested that machine learning could better help sort the sunshine from the sleet. Google is the latest firm to get involved, and in a blog post this week shared new research that it says enables “nearly instantaneous” weather forecasts.

The work is in the early stages and has yet to be integrated into any commercial systems, but early results look promising. In the non-peer-reviewed paper, Google’s researchers describe how they were able to generate accurate rainfall predictions up to six hours ahead of time at a 1km resolution from just “minutes” of calculation.

That’s a big improvement over existing techniques, which can take hours to generate forecasts, although they do so over longer time periods and generate more complex data.

Speedy predictions, say the researchers, will be “an essential tool needed for effective adaptation to climate change, particularly for extreme weather.” In a world increasingly dominated by unpredictable weather patterns, they say, short-term forecasts will be crucial for “crisis management, and the reduction of losses to life and property.”

This seems to be the sweet spot for machine learning in weather forecasts right now: making speedy, short-term predictions, while leaving longer forecasts to more powerful models. NOAA’s weather models, for example, create forecasts up to 10 days in advance.

While we’ve not yet seen the full effects of AI on weather forecasting, plenty of other companies are also investigating this same area, including IBM and Monsanto. And, as Google’s researchers point out, such forecasting techniques are only going to become more important in our daily lives as we feel the effects of climate change.

Information Management (NEWS)

“Artificial intelligence is not there to replace journalists or eliminate jobs”, according to Francesco Marconi, a professor of journalism at Columbia University in New York believes that only eight to 12 per cent of reporters’ current tasks will be taken over by machines, which will in fact reorient editors and journalists towards value-added content: long-form journalism, feature interviews, analysis, data-driven journalism and investigative journalism.

At the moment, AI robots perform basic tasks like writing two to six paragraphs on sports scores and quarterly earnings reports at the Associated Press, election results in Switzerland and Olympic results at the Washington Post. The outcomes are convincing, but they also show the limits of AI.

AI robots analyzing large databases can send journalists at Bloomberg News an alert as soon as a trend or anomaly emerges from big data.

AI can also save reporters a lot of time by transcribing audio and video interviews. AFP has a tool for that. The same is true for major reports on pollution or violence, which rely on vast databases. The machines can analyze complex data in no time at all.

Afterwards, the journalist does his or her essential work of fact-checking, analyzing, contextualizing and gathering information. AI can hardly replace this. In this sense, humans must remain central to the entire journalistic process.

Industry 4.0

Automation and robotics provide the muscle for Industry 4.0, AR/VR, cameras and other sensors provide the senses, and data and connectivity are its central nervous system. But the real brains behind this industrial revolution is AI (Artificial Intelligence) and this is underlined by numerous offerings that use AI to enhance processes driven by the collaboration between human and machine.

Cyber-physical systems and machine learning are cornerstones of the Industry 4.0 philosophy, first promoted at the Hannover Fair some nine years ago.  When it was first presented to the market it was met with enthusiasm and confusion, often in equal measure.  If anything, those behind the promotion of this industrial revolution were anticipating or waiting for, certain technology to be ready to deliver the promised values that come from systems that self-heal and self-learn to improve efficiency and outcome.

In 2017 at CES, Brian Krzanich, Intel CEO, tells an audience that he could envisage a time when intelligent systems could predict need or market opportunity and design and manufacture products with little or no human interface.  Imagine Marketing, Supply Chain and Manufacturing all being tied off this way! This is a long way away, but AI is already finding its way into the Smart Factory environment, and many companies are working hard to harness this technology along with AR and other enabling technologies to make a more agile manufacturing process for our customers.

Companies are already using cyber-physical systems or closed loops in various parts of our manufacturing processes and their Industry 4.0 team works in all their manufacturing facilities to bring the best and brightest ideas to our global footprint.  Team are actively working with trade associations like the IPC to develop and deliver standards for machine-to-machine communication that will drive this industrial revolution forward.

Autonomous Vehicles

Autonomous cars have been recently hitting the headlines and dominating tech-talks. They are seen as a post-Uber disruption to public commute and transportation of goods. Surely they are no figment of imagination in the age of artificial intelligence (AI), which is being used to complement driverless cars.

Companies such as Waymo and Tesla are heavily invested in driverless cars. Currently, Waymo has begun testing of driverless cars again after stopping in 2017. Testing is done with drivers inside the vehicles until the company is able to gain enough data to move towards a completely driverless solution.

Subject to regulatory and social acceptance, the impact of completely autonomous cars is not limited to the disruption of the public transport system, but also its potential to shake other industries. 

When talking about autonomous cars, it is almost impossible not to discuss artificial intelligence. AI is used to enable the cars to navigate through the traffic and handle complex situations. Also, with a combined AI software and other IoT sensors, such as cameras, it becomes easier to ensure proper and safe driving.

AI is advancing autonomous driving for people to experience of effortless transit. Governments, too, have jumped into the race, wooing investors to bring AI-based driverless cars into commercial use.

In Aug. 2018, the British Government unveiled plans for an AI simulator, intended for the purpose of attracting companies as a favorable destination for testing self-driving cars. Named OmniCAV, the simulator can recreate a virtual version of 32km of Oxfordshire roads.

The world is changing, and AI is getting smarter every day. But while we might be around the corner from witnessing the post-Uber era, don’t hold your breath: fully autonomous driving still has a long road to go.

Conclusions

The future visions outlined here are complementary and based on our current and foreseeable understanding of AI. There are unknown unknowns, of course, but all one can say about them is precisely this: they exist, and we have no idea about them. It is a bit like saying that we know there are questions we are not asking but cannot say what these questions are. The future of AI is full of unknown unknowns. What I have tried to do in this article is to look at the “seeds of time” that we have already sowed. I have concentrated on the nature of data and of problems because the former are what enable AI to work, and the latter provide the boundaries within which AI can work successfully. At this level of abstraction, two conclusions seem to be very plausible. We will seek to develop AI by using data that are as much as possible hybrid and preferably synthetic, through a process of ludification of interactions and tasks. In other words, the tendency will be to try to move away from purely historical data whenever possible. And we will do so by translating as much as possible difficult problems into complex problems, through the enveloping of realities around the skills of our artifacts. In short, we will seek to create hybrid or synthetic data to deal with complex problems, by ludifying tasks and interactions in enveloped environments.

————————–MJM ————————–

This ‘Artificial Intelligence Primer’ is a mash-up of some very powerful thinking by many great minds, who are cited below. It is offered to construct a foundation knowledge and a more precise understanding of what AI is (What?) and how it will affect our lives (So What?). However, the real question for you to ponder about artificial intelligence is, “What’s Next?”

————————–MJM ————————–

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————————–MJM ————————–

About the Author:

Michael Martin has more than 35 years of experience in systems design for applications that use broadband networks, optical fibre, wireless, and digital communications technologies. He is a business and technology consultant. He offers his services on a contracting basis. Over the past 15 years with IBM, he has worked in the GBS Global Center of Competency for Energy and Utilities and the GTS Global Center of Excellence for Energy and Utilities. He is a founding partner and President of MICAN Communications and before that was President of Comlink Systems Limited and Ensat Broadcast Services, Inc., both divisions of Cygnal Technologies Corporation (CYN: TSX). Martin currently serves on the Board of Directors for TeraGo Inc (TGO: TSX) and previously served on the Board of Directors for Avante Logixx Inc. (XX: TSX.V).  He has served as a Member, SCC ISO-IEC JTC 1/SC-41 – Internet of Things and related technologies, ISO – International Organization for Standardization, and as a member of the NIST SP 500-325 Fog Computing Conceptual Model, National Institute of Standards and Technology. He served on the Board of Governors of the University of Ontario Institute of Technology (UOIT) [now OntarioTech University] and on the Board of Advisers of five different Colleges in Ontario.  For 16 years he served on the Board of the Society of Motion Picture and Television Engineers (SMPTE), Toronto Section.  He holds three master’s degrees, in business (MBA), communication (MA), and education (MEd). As well, he has three undergraduate diplomas and five certifications in business, computer programming, internetworking, project management, media, photography, and communication technology. He has earned 20 badges in next generation MOOC continuous education in IoT, Cloud, AI and Cognitive systems, Blockchain, Agile, Big Data, Design Thinking, Security, and more.