AI News, Image Recognition Demystified artificial intelligence

Artificial Intelligence — Demystified

This is possibly due to media hype, differing definitions, technical jargon and images projected by science fiction about AI.

The term “Intelligence” describes the cognitive function of humans (and animals) of becoming aware of situations, learning from them, and applying the learning to make decisions and solve new problems.

It includes one’s capacity for logic, understanding, self-awareness, learning, emotional knowledge, planning, creativity, and problem-solving.

Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem-solving”.

Professor Linda Gottfredson (University of Deleware) puts it very well: “Cognition is the ability to learn and learn from experience, think abstractly, comprehend complex ideas, reason, plan and solve problems.” Is your personal computer intelligent?

Many household gadgets like washing machines, dishwashers, air conditioners, and even personal computers seem to be intelligent in the sense that they automatically perform complex logical tasks.

Every person develops their unique algorithms for dealing with life through their subjective learning and life experiences — that is why we often make different decisions when faced with a similar choice of options.

AI automates decision-making Functionally, AI is an automated decision-making system for a specific area of expertise — like medical, taxes, investment, translation, speech recognition or face detection.

AI figures out the best answer or decision (with the highest probability of success) for any given new situation within the context of its expertise.

To illustrate that AI is essentially an information service needing no physical body, here are a few examples of how AI is embedded in specific services: Evolution of AI We have been exploring the topic of AI since the time of Alan Turing in the mid-last century.

He developed in 1950 the famous “Turing Test” to test a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.

Significant progress has been made in the last 5–10 years due to advances in machine learning and deep neural networks, leading to a range of services (some shown above) we all can experience today.

1Artificial Narrow Intelligence (ANI): In this stage, AI is focused on one specific area like translation, face recognition, playing chess, diagnosing cancer, interpreting radiology images, stock investment etc.

In the next 3–5 years, many new products and services will integrate ANI features offering unique customer value — a huge business opportunity to differentiate.

3Artificial Super-Intelligence (ASI): AI expert Nick Bostrom defines Super-intelligence as “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom, and social skills.” Much better can mean 10x, 1000x, or 1000000x.

AI is our teammate In conclusion, AI is essentially a machine capable of very efficiently performing rational skills like learning, decision-making, and problem solving, which we have traditionally associated exclusively with humans.

Demystifying Artificial Intelligence (AI)

When we’re asked, “What is artificial intelligence?” many mental images come to mind.

NLP is used to translate plain-English search terms into legal searches on research platforms such as Thomson Reuters Westlaw, and also to analyze language in documents to make sense of them for ediscovery or due diligence reviews.

The examples below show everyday examples of AI – whether it’s Amazon’s talking machine (Alexa), or Spotify® building you a recommended music playlist based on your listening habits.

With tools like translation software and data-to-text generation tools improving every day, machines are rapidly expanding the ways that Natural language processing serves our needs.

You’d be surprised to know how much of the perfectly readable news you consume online or in print about sports or financial reports, for example, is actually generated by machines and not human journalists.

What’s helpful for our professional lives is that we’re starting to leverage AI across a broad range of applications: legal research, litigation strategy, ediscovery, self-help online legal services, dispute-resolution models, and contract review and analysis.

As the examples below show, AI tends to be applicable in cases where there are standard and often recurring questions that need to be answered, paired with significant data sets likely to hold those answers.

It can provide specific answers to common, well-defined types of legal questions – about statutes of limitations, for instance, or the elements of specific causes of action.

Because business operations are going digital across the board, today’s organizations generate exponentially growing volumes of data – most of it unstructured or semi-structured data in the form of email, written memoranda and documents, spreadsheets, calendars, and so on.

Luckily, machines can pick up the slack, and machine learning can help “teach” document-review software how to predict whether a given document is likely to be responsive to a given request.

You see this online legal service being applied in fields as complex as data privacy regulations – or as simple as consumer-facing chatbots used to challenge parking tickets.

Many law firms are investing in this type of client-facing AI, but it’s also being employed in services offered by other types of organizations: chatbot providers, legal services organizations, courts, and other government entities.

“What risks or opportunities lie in these thousands of contracts?” Another application where lawyers need to conduct large-scale review of document sets is in contract analysis, particularly for due diligence reviews in large mergers and acquisitions.

they’re text-based documents, not rows and columns of data, but they do share certain common clauses and terms that help computers identify specific clauses and potential outliers.

Demystifying artificial intelligence What business leaders need to know about cognitive technologies

Examples include tasks such as visual perception, speech recognition, decision making under uncertainty, learning, and translation between languages.13 Defining AI in terms of the tasks humans do, rather than how humans think, allows us to discuss its practical applications today, well before science arrives at a definitive understanding of the neurological mechanisms of intelligence.14 It is worth noting that the set of tasks that normally require human intelligence is subject to change as computer systems able to perform those tasks are invented and then widely diffused.

The history of the field is marked by “periods of hype and high expectations alternating with periods of setback and disappointment,” as a recent apt summation puts it.16 After articulating the bold goal of simulating human intelligence in the 1950s, researchers developed a range of demonstration programs through the 1960s and into the '70s that showed computers able to accomplish a number of tasks once thought to be solely the domain of human endeavor, such as proving theorems, solving calculus problems, responding to commands by planning and performing physical actions—even impersonating a psychotherapist and composing music.

The 1980s saw the launch of commercial vendors of AI technology products, some of which had initial public offerings, such as Intellicorp, Symbolics,17 and Teknowledge.18 By the end of the 1980s, perhaps half of the Fortune 500 were developing or maintaining “expert systems,” an AI technology that models human expertise with a knowledge base of facts and rules.19 High hopes for the potential of expert systems were eventually tempered as their limitations, including a glaring lack of common sense, the difficulty of capturing experts’ tacit knowledge, and the cost and complexity of building and maintaining large systems, became widely recognized.

Classification techniques may be used to determine if the features identified in an image are likely to represent a kind of object already known to the system.30 Computer vision has diverse applications, including analyzing medical imaging to improve prediction, diagnosis, and treatment of diseases;31 face recognition, used by Facebook to automatically identify people in photographs32 and in security and surveillance to spot suspects;33 and in shopping—consumers can now use smartphones to photograph products and be presented with options for purchasing them.34 Machine vision, a related discipline, generally refers to vision applications in industrial automation, where computers recognize objects such as manufactured parts in a highly constrained factory environment—rather simpler than the goals of computer vision, which seeks to operate in unconstrained environments.

While computer vision is an area of ongoing computer science research, machine vision is a “solved problem”—the subject not of research but of systems engineering.35 Because the range of applications for computer vision is expanding, startup companies working in this area have attracted hundreds of millions of dollars in venture capital investment since 2011.36 Machine learning refers to the ability of computer systems to improve their performance by exposure to data without the need to follow explicitly programmed instructions.

Machine learning techniques often play a role in other cognitive technologies such as computer vision, which can train vision models on a large database of images to improve their ability to recognize classes of objects.37 Machine learning is one of the hottest areas in cognitive technologies today, having attracted around a billion dollars in venture capital investment between 2011 and mid-2014.38 Google is said to have invested some $400 million to acquire DeepMind, a machine learning company, in 2014.39 Natural language processing refers to the ability of computers to work with text the way humans do, for instance, extracting meaning from text or even generating text that is readable, stylistically natural, and grammatically correct.

Classification, powered by machine learning, would then operate on the extracted features to classify a message as spam or not.41 Because context is so important for understanding why “time flies” and “fruit flies” are so different, practical applications of natural language processing often address relative narrow domains such as analyzing customer feedback about a particular product or service,42 automating discovery in civil litigation or government investigations (e-discovery),43and automating writing of formulaic stories on topics such as corporate earnings or sports.44 Robotics, by integrating cognitive technologies such as computer vision and automated planning with tiny, high-performance sensors, actuators, and cleverly designed hardware, has given rise to a new generation of robots that can work alongside people and flexibly perform many different tasks in unpredictable environments.45 Examples include unmanned aerial vehicles,46 “cobots” that share jobs with humans on the factory floor,47 robotic vacuum cleaners,48and a slew of consumer products, from toys to home helpers.49 Speech recognition focuses on automatically and accurately transcribing human speech.

In banking, automated fraud detection systems use machine learning to identify behavior patterns that could indicate fraudulent payment activity, speech recognition technology to automate customer service telephone interactions, and voice recognition technology to verify the identity of callers.55 In health care, automatic speech recognition for transcribing notes dictated by physicians is used in around half of US hospitals, and its use is growing rapidly.56 Computer vision systems automate the analysis of mammograms and other medical images.57 IBM’s Watson uses natural language processing to read and understand a vast medical literature, hypothesis generation techniques to automate diagnosis, and machine learning to improve its accuracy.58 In life sciences, machine learning systems are being used to predict cause-and-effect relationships from biological data59 and the activities of compounds,60helping pharmaceutical companies identify promising drugs.61 In media and entertainment, a number of companies are using data analytics and natural language generation technology to automatically draft articles and other narrative material about data-focused topics such as corporate earnings or sports game summaries.62 Oil and gas producers use machine learning in a wide range of applications, from locating mineral deposits63 to diagnosing mechanical problems with drilling equipment.64 The public sector is adopting cognitive technologies for a variety of purposes including surveillance, compliance and fraud detection, and automation.

The state of Georgia, for instance, employs a system combining automated handwriting recognition with crowdsourced human assistance to digitize financial disclosure and campaign contribution forms.65 Retailers use machine learning to automatically discover attractive cross-sell offers and effective promotions.66 Technology companies are using cognitive technologies such as computer vision and machine learning to enhance products or create entirely new product categories, such as the Roomba robotic vacuum cleaner67 or the Nest intelligent thermostat.68 As the examples above show, the potential business benefits of cognitive technologies are much broader than cost savings that may be implied by the term “automation.” They include: The impact of cognitive technologies on business should grow significantly over the next five years.

Professional translators regularly rely on machine translation, for instance, to improve their efficiency, automating routine translation tasks so they can focus on the challenging ones.77 From 2011 through May 2014, over $2 billion dollars in venture capital funds have flowed to companies building products and services based on cognitive technologies.78 During this same period, over 100 companies merged or were acquired, some by technology giants such as Amazon, Apple, IBM, Facebook, and Google.79 All of this investment has nurtured a diverse landscape of companies that are commercializing cognitive technologies.

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