Artificial Intelligence - Who Was Alan Turing?

 

 

 Alan Mathison Turing OBE FRS(1912–1954) was a logician and mathematician from the United Kingdom.

He is known as the "Father of Artificial Intelligence" and "The Father of Computer Science." 

Turing earned a first-class honors degree in mathematics from King's College, Cambridge, in 1934.

Turing received his PhD from Princeton University after a fellowship at King's College, where he studied under American mathematician Alonzo Church.

Turing wrote numerous important publications during his studies, including "On Computable Numbers, with an Application to the Entscheidungsproblem," which proved that the so-called "decision problem" had no solution.

The decision issue asks if there is a method for determining the correctness of any assertion inside a mathematical system.

This paper also explored a hypothetical Turing machine (basically an early computer) that, if represented by an algorithm, could execute any mathematical operation.

Turing is best known for his codebreaking work at Bletchley Park's Government Code and Cypher School (GC&CS) during World War II (1939–1945).

Turing's work at GC&CS included heading Hut 8, which was tasked with cracking the German Enigma and other very difficult naval encryption.

Turing's work undoubtedly shortened the war by years, saving millions of lives, but it is hard to measure with precision.

Turing wrote "The Applications of Probability to Cryptography" and "Paper on Statistics of Repetitions" during his tenure at GC&CS, both of which were held secret for seventy years by the Government Communications Headquarters (GCHQ) until being given to the UK National Archives in 2012.

Following WWII, Turing enrolled at the Victoria University of Manchester to study mathematical biology while continuing his work in mathematics, stored-program digital computers, and artificial intelligence.

Turing's 1950 paper "Computing Machinery and Intelligence" looked into artificial intelligence and introduced the concept of the Imitation Game (also known as the Turing Test), in which a human judge uses a set of written questions and responses to try to distinguish between a computer program and a human.

If the computer program imitates a person to the point that the human judge cannot discern the difference between the computer program's and the human's replies, the program has passed the test, indicating that it is capable of intelligent reasoning.

Turochamp, a chess program written by Turing and his colleague D.G. Champernowne, was meant to be executed by a computer, but no machine with adequate capacity existed to test the program.

Turing instead manually ran the algorithms to test the software.

Turing was well-recognized during his lifetime, despite the fact that most of his work remained secret until after his death.

Turing was made a Fellow of the Royal Society in 1951 and was awarded to the Order of the British Empire in 1946.(FRS).

The Turing Award, named after him, is given annually by the Association for Computing Machinery for contributions to the area of computing.

The Turing Award, which comes with a $1 million reward, is commonly recognized as the Nobel Prize of Computing.

Turing was outspoken about his sexuality at a period when homosexuality was still illegal in the United Kingdom.

Turing was accused in 1952 under Section 11 of the Criminal Law Amendment Act 1885 with "gross indecency." 

Turing was found guilty, granted probation, and was sentenced to a year of "chemical castration," in which he was injected with synthetic estrogen.

Turing's conviction had an influence on his career as well.

His security clearance was withdrawn, and he was compelled to stop working for the GCHQ as a cryptographer.

Following successful campaigning for an apology and pardon, the British government passed the Alan Turing bill in 2016, which retrospectively pardoned hundreds of persons imprisoned under Section 11 and other historical laws.

In 1954, Turing died of cyanide poisoning.

Turing's death may have been caused by inadvertent inhalation of cyanide vapors, despite the fact that it was officially considered a suicide.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Chatbots and Loebner Prize; General and Narrow AI; Moral Turing Test; Turing Test.

References And Further Reading

Hodges, Andrew. 2004. “Turing, Alan Mathison (1912–1954).” In Oxford Dictionary of National Biography. https://www.oxforddnb.com/view/10.1093/ref:odnb/9780198614128.001.0001/odnb-9780198614128-e-36578.

Lavington, Simon. 2012. Alan Turing and His Contemporaries: Building the World’s First Computers. Swindon, UK: BCS, The Chartered Institute for IT.

Sharkey, Noel. 2012. “Alan Turing: The Experiment that Shaped Artificial Intelligence.” BBC News, June 21, 2012. https://www.bbc.com/news/technology-18475646.

Artificial Intelligence - What Is Cognitive Computing?

 

Self-learning hardware and software systems that use machine learning, natural language processing, pattern recognition, human-computer interaction, and data mining technologies to mimic the human brain are referred to as cognitive computing.

The term "cognitive computing" refers to the use of advances in cognitive science to create new and complex artificial intelligence systems.

Cognitive systems aren't designed to take the place of human thinking, reasoning, problem-solving, or decision-making; rather, they're meant to supplement or aid people.

A collection of strategies to promote the aims of affective computing, which entails narrowing the gap between computer technology and human emotions, is frequently referred to as cognitive computing.

Real-time adaptive learning approaches, interactive cloud services, interactive memo ries, and contextual understanding are some of these methodologies.

To conduct quantitative assessments of organized statistical data and aid in decision-making, cognitive analytical tools are used.

Other scientific and economic systems often include these tools.

Complex event processing systems utilize complex algorithms to assess real-time data regarding events for patterns and trends, offer choices, and make judgments.

These kinds of systems are widely used in algorithmic stock trading and credit card fraud detection.

Face recognition and complex image recognition are now possible with image recognition systems.

Machine learning algorithms build models from data sets and improve as new information is added.

Neural networks, Bayesian classifiers, and support vector machines may all be used in machine learning.

Natural language processing entails the use of software to extract meaning from enormous amounts of data generated by human conversation.

Watson from IBM and Siri from Apple are two examples.

Natural language comprehension is perhaps cognitive computing's Holy Grail or "killer app," and many people associate natural language processing with cognitive computing.

Heuristic programming and expert systems are two of the oldest branches of so-called cognitive computing.

Since the 1980s, there have been four reasonably "full" cognitive computing architectures: Cyc, Soar, Society of Mind, and Neurocognitive Networks.

Speech recognition, sentiment analysis, face identification, risk assessment, fraud detection, and behavioral suggestions are some of the applications of cognitive computing technology.

These applications are referred regarded as "cognitive analytics" systems when used together.

In the aerospace and defense industries, agriculture, travel and transportation, banking, health care and the life sciences, entertainment and media, natural resource development, utilities, real estate, retail, manufacturing and sales, marketing, customer service, hospitality, and leisure, these systems are in development or are being used.

Netflix's movie rental suggestion algorithm is an early example of predictive cognitive computing.

Computer vision algorithms are being used by General Electric to detect tired or distracted drivers.

Customers of Domino's Pizza can place orders online by speaking with a virtual assistant named Dom.

Elements of Google Now, a predictive search feature that debuted in Google applications in 2012, assist users in predicting road conditions and anticipated arrival times, locating hotels and restaurants, and remembering anniversaries and parking spots.

In IBM marketing materials, the term "cognitive" computing appears frequently.

Cognitive computing, according to the company, is a subset of "augmented intelligence," which is preferred over artificial intelligence.

The Watson machine from IBM is frequently referred to as a "cognitive computer" since it deviates from the traditional von Neumann design and instead draws influence from neural networks.

Neuroscientists are researching the inner workings of the human brain, seeking for connections between neuronal assemblies and mental aspects, and generating new mental ideas.

Hebbian theory is an example of a neuroscientific theory that underpins cognitive computer machine learning implementations.

The Hebbian theory is a proposed explanation for neural adaptation during the learning process.

Donald Hebb initially proposed the hypothesis in his 1949 book The Organization of Behavior.

Learning, according to Hebb, is a process in which the causal induction of recurrent or persistent neuronal firing or activity causes neural traces to become stable.

"Any two cells or systems of cells that are consistently active at the same time will likely to become'associated,' such that activity in one favors activity in the other," Hebb added (Hebb 1949, 70).

"Cells that fire together, wire together," is how the idea is frequently summarized.

According to this hypothesis, the connection of neuronal cells and tissues generates neurologically defined "engrams" that explain how memories are preserved in the brain as biophysical or biochemical changes.

Engrams' actual location, as well as the procedures by which they are formed, are currently unknown.

IBM machines are stated to learn by aggregating information into a computational convolution or neural network architecture made up of weights stored in a parallel memory system.

Intel introduced Loihi, a cognitive chip that replicates the functions of neurons and synapses, in 2017.

Loihi is touted to be 1,000 times more energy efficient than existing neurosynaptic devices, with 128 clusters of 1,024 simulated neurons on per chip, for a total of 131,072 simulated neurons.

Instead of relying on simulated neural networks and parallel processing with the overarching goal of developing artificial cognition, Loihi uses purpose-built neural pathways imprinted in silicon.

These neuromorphic processors are likely to play a significant role in future portable and wire-free electronics, as well as automobiles.

Roger Schank, a cognitive scientist and artificial intelligence pioneer, is a vocal opponent of cognitive computing technology.

"Watson isn't thinking. You can only reason if you have objectives, plans, and strategies to achieve them, as well as an understanding of other people's ideas and a knowledge of prior events to draw on.

"Having a point of view is also beneficial," he writes.

"How does Watson feel about ISIS, for example?" Is this a stupid question? ISIS is a topic on which actual thinking creatures have an opinion" (Schank 2017).

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Computational Neuroscience; General and Narrow AI; Human Brain Project; SyNAPSE.

Further Reading

Hebb, Donald O. 1949. The Organization of Behavior. New York: Wiley.

Kelly, John, and Steve Hamm. 2013. Smart Machines: IBM’s Watson and the Era of Cognitive Computing. New York: Columbia University Press.

Modha, Dharmendra S., Rajagopal Ananthanarayanan, Steven K. Esser, Anthony Ndirango, Anthony J. Sherbondy, and Raghavendra Singh. 2011. “Cognitive Computing.” Communications of the ACM 54, no. 8 (August): 62–71.

Schank, Roger. 2017. “Cognitive Computing Is Not Cognitive at All.” FinTech Futures, May 25. https://www.bankingtech.com/2017/05/cognitive-computing-is-not-cognitive-at-all

Vernon, David, Giorgio Metta, and Giulio Sandini. 2007. “A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents.” IEEE Transactions on Evolutionary Computation 11, no. 2: 151–80.

Artificial Intelligence - Who Is Ray Kurzweil (1948–)?

Ray Kurzweil is a futurist and inventor from the United States.

He spent the first half of his career developing the first CCD flat-bed scanner, the first omni-font optical character recognition device, the first print-to-speech reading machine for the blind, the first text-to-speech synthesizer, the first music synthesizer capable of recreating the grand piano and other orchestral instruments, and the first commercially marketed, large-vocabulary speech recognition machine.

He has earned several awards for his contributions to technology, including the Technical Grammy Award in 2015 and the National Medal of Technology.

Kurzweil is the cofounder and chancellor of Singularity University, as well as the director of engineering at Google, where he leads a team that works on artificial intelligence and natural language processing.

Singularity University is a non-accredited graduate school founded on the premise of tackling great issues like renewable energy and space travel by gaining a deep understanding of the opportunities presented by technology progress's current acceleration.

The university, which is headquartered in Silicon Valley, has evolved to include one hundred chapters in fifty-five countries, delivering seminars, educational programs, and business acceleration programs.

While at Google, Kurzweil published the book How to Create a Mind (2012).

He claims that the neo cortex is a hierarchical structure of pattern recognizers in his Pattern Recognition Theory of Mind.

Kurzweil claims that replicating this design in machines might lead to the creation of artificial superintelligence.

He believes that by doing so, he will be able to bring natural language comprehension to Google.

Kurzweil's popularity stems from his work as a futurist.

Futurists are those who specialize in or are interested in the near-to-long-term future and associated topics.

They use well-established methodologies like scenario planning to carefully examine forecasts and construct future possibilities.

Kurzweil is the author of five national best-selling books, including The Singularity Is Near, which was named a New York Times best-seller (2005).

He has an extensive list of forecasts.

Kurzweil predicted the enormous development of international internet usage in the second part of the decade in his debut book, The Age of Intelligent Machines (1990).

He correctly predicted that computers will soon exceed humans in making the greatest investing choices in his second extremely important book, The Age of Spiritual Machines (where "spiritual" stands for "aware"), published in 1999.

Kurzweil prophesied in the same book that computers would one day "appear to have their own free will" and perhaps have "spiritual experiences" (Kurz weil 1999, 6).

Human-machine barriers will dissolve to the point that they will basically live forever as combined human-machine hybrids.

Scientists and philosophers have slammed Kurzweil's forecast of a sentient computer, claiming that awareness cannot be created by calculations.

Kurzweil tackles the phenome non of the Technological Singularity in his third book, The Singularity Is Near.

John von Neumann, a famous mathematician, created the word singularity.

In a 1950s chat with his colleague Stanislaw Ulam, von Neumann proposed that the ever-accelerating speed of technological progress "appears to be reaching some essential singularity in the history of the race beyond which human activities as we know them could not continue" (Ulam 1958, 5).

To put it another way, technological development would alter the course of human history.

Vernor Vinge, a computer scientist, math professor, and science fiction writer, rediscovered the word in 1993 and utilized it in his article "The Coming Technological Singularity." In Vinge's article, technological progress is more accurately defined as an increase in processing power.

Vinge investigates the idea of a self-improving artificial intelligence agent.

According to this theory, the artificial intelligent agent continues to update itself and grow technologically at an unfathomable pace, eventually resulting in the birth of a superintelligence—that is, an artificial intelligence that far exceeds all human intelligence.

In Vinge's apocalyptic vision, robots first become autonomous, then superintelligent, to the point where humans lose control of technology and machines seize control of their own fate.

Machines will rule the planet because technology is more intelligent than humans.

According to Vinge, the Singularity is the end of the human age.

Kurzweil presents an anti-dystopic Singularity perspective.

Kurzweil's core premise is that humans can develop something smarter than themselves; in fact, exponential advances in computer power make the creation of an intelligent machine all but inevitable, to the point that the machine will surpass humans in intelligence.

Kurzweil believes that machine intelligence and human intellect will converge at this moment.

The subtitle of The Singularity Is Near is When Humans Transcend Biology, which is no coincidence.

Kurzweil's overarching vision is based on discontinuity: no lesson from the past, or even the present, can aid humans in determining the way to the future.

This also explains why new types of education, such as Singularity University, are required.

Every sentimental look back to history, every memory of the past, renders humans more susceptible to technological change.

With the arrival of a new superintelligent, almost immortal race, history as a human construct will soon come to an end.

Posthumans, the next phase in human development, are known as immortals.

Kurzweil believes that posthumanity will be made up of sentient robots rather than people with mechanical bodies.

He claims that the future should be formed on the assumption that mankind is in the midst of an extraordinary period of technological advancement.

The Singularity, he believes, would elevate humanity beyond its wildest dreams.

While Kurzweil claims that artificial intelligence is now outpacing human intellect on certain activities, he also acknowledges that the moment of superintelligence, often known as the Technological Singularity, has not yet arrived.

He believes that individuals who embrace the new age of human-machine synthesis and are daring to go beyond evolution's boundaries would view humanity's future as positive. 

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

General and Narrow AI; Superintelligence; Technological Singularity.

Further Reading:

Kurzweil, Ray. 1990. The Age of Intelligent Machines. Cambridge, MA: MIT Press.

Kurzweil, Ray. 1999. The Age of Spiritual Machines: When Computers Exceed Human Intelligence. New York: Penguin.

Kurzweil, Ray. 2005. The Singularity Is Near: When Humans Transcend Biology. New York: Viking.

Ulam, Stanislaw. 1958. “Tribute to John von Neumann.” Bulletin of the American Mathematical Society 64, no. 3, pt. 2 (May): 1–49.

Vinge, Vernor. 1993. “The Coming Technological Singularity: How to Survive in the Post-Human Era.” In Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace, 11–22. Cleveland, OH: NASA Lewis Research Center.

 

Artificial Intelligence - Speech Recognition And Natural Language Processing

 

Natural language processing (NLP) is a branch of artificial intelligence that entails mining human text and voice in order to produce or reply to human enquiries in a legible or natural manner.

To decode the ambiguities and opacities of genuine human language, NLP has needed advances in statistics, machine learning, linguistics, and semantics.

Chatbots will employ natural language processing to connect with humans across text-based and voice-based interfaces in the future.

Interactions between people with varying talents and demands will be supported by computer assistants.

By making search more natural, they will enable natural language searches of huge volumes of information, such as that found on the internet.

They may also incorporate useful ideas or nuggets of information into a variety of circumstances, including meetings, classes, and informal discussions.

They may even be able to "read" and react in real time to the emotions or moods of human speakers (so-called "sentient analysis").

By 2025, the market for NLP hardware, software, and services might be worth $20 billion per year.

Speech recognition, often known as voice recognition, has a long history.

Harvey Fletcher, a physicist who pioneered research showing the link between voice energy, frequency spectrum, and the perception of sound by a listener, initiated research into automated speech recognition and transcription at Bell Labs in the 1930s.

Most voice recognition algorithms nowadays are based on his research.

Homer Dudley, another Bell Labs scientist, received patents for a Vodor voice synthesizer that imitated human vocalizations and a parallel band pass vocodor that could take sound samples and put them through narrow band filters to identify their energy levels by 1940.

By putting the recorded energy levels through various filters, the latter gadget might convert them back into crude approximations of the original sounds.

Bell Labs researchers had found out how to make a system that could do more than mimic speech by the 1950s.

During that decade, digital technology had progressed to the point that the system could detect individual spoken word portions by comparing their frequencies and energy levels to a digital sound reference library.

In essence, the system made an informed guess about the expression being expressed.

The pace of change was gradual.

Bell Labs robots could distinguish around 10 syllables uttered by a single person by the mid-1950s.

Researchers at MIT, IBM, Kyoto University, and University College London were working on recognizing computers that employed statistics to detect words with numerous phonemes toward the end of the decade.

Phonemes are sound units that are perceived as separate from one another by listeners.

Additionally, progress was being made on systems that could recognize the voice of many speakers.

Allen Newell headed the first professional automated speech recognition group, which was founded in 1971.

The research team split their time between acoustics, parametrics, phonemics, lexical ideas, sentence processing, and semantics, among other levels of knowledge generation.

Some of the issues examined by the group were investigated via funds from the Defense Advanced Research Project Agency in the 1970s (DARPA).

DARPA was intrigued in the technology because it might be used to handle massive amounts of spoken data generated by multiple government departments and transform that data into insights and strategic solutions to challenges.

Techniques like dynamic temporal warping and continuous voice recognition have made progress.

Computer technology progressed significantly, and numerous mainframe and minicomputer manufacturers started to perform research in natural language processing and voice recognition.

The Speech Understanding Research (SUR) project at Carnegie Mellon University was one of the DARPA-funded projects.

The SUR project, directed by Raj Reddy, produced numerous groundbreaking speech recognition systems, including Hearsay, Dragon, Harpy, and Sphinx.

Harpy is notable in that it employs the beam search approach, which has been a standard in such systems for decades.

Beam search is a heuristic search technique that examines a network by extending the most promising node among a small number of possibilities.

Beam search is an improved version of best-first search that uses less memory.

It's a greedy algorithm in the sense that it uses the problem-solving heuristic of making the locally best decision at each step in the hopes of obtaining a global best choice.

In general, graph search algorithms have served as the foundation for voice recognition research for decades, just as they have in the domains of operations research, game theory, and artificial intelligence.

By the 1980s and 1990s, data processing and algorithms had advanced to the point where researchers could use statistical models to identify whole strings of words, even phrases.

The Pentagon remained the field's leader, but IBM's work had progressed to the point where the corporation was on the verge of manufacturing a computerized voice transcription device for its corporate clients.

Bell Labs had developed sophisticated digital systems for automatic voice dialing of telephone numbers.

Other applications that seemed to be within reach were closed captioned transcription of television broadcasts and personal automatic reservation systems.

The comprehension of spoken language has dramatically improved.

The Air Travel Information System was the first commercial system to emerge from DARPA funding (ATIS).

New obstacles arose, such as "disfluencies," or natural pauses, corrections, casual speech, interruptions, and verbal fillers like "oh" and "um" that organically formed from conversational speaking.

Every Windows 95 operating system came with the Speech Application Programming Interface (SAPI) in 1995.

SAPI (which comprised subroutine definitions, protocols, and tools) made it easier for programmers and developers to include speech recognition and voice synthesis into Windows programs.

Other software developers, in particular, were given the option to construct and freely share their own speech recognition engines thanks to SAPI.

It gave NLP technology a big boost in terms of increasing interest and generating wider markets.

The Dragon line of voice recognition and dictation software programs is one of the most well-known mass-market NLP solutions.

The popular Dragon NaturallySpeaking program aims to provide automatic real-time, large-vocabulary, continuous-speech dictation with the use of a headset or microphone.

The software took fifteen years to create and was initially published in 1997.

It is still widely regarded as the gold standard for personal computing today.

One hour of digitally recorded speech takes the program roughly 4–8 hours to transcribe, although dictation on screen is virtually instantaneous.

Similar software is packaged with voice dictation functions in smart phones, which converts regular speech into text for usage in text messages and emails.

The large amount of data accessible on the cloud, as well as the development of gigantic archives of voice recordings gathered from smart phones and electronic peripherals, have benefited industry tremendously in the twenty-first century.

Companies have been able to enhance acoustic and linguistic models for voice processing as a result of these massive training data sets.

To match observed and "classified" sounds, traditional speech recognition systems employed statistical learning methods.

Since the 1990s, more Markovian and hidden Markovian systems with reinforcement learning and pattern recognition algorithms have been used in speech processing.

Because of the large amounts of data available for matching and the strength of deep learning algorithms, error rates have dropped dramatically in recent years.

Despite the fact that linguists argue that natural languages need flexibility and context to be effectively comprehended, these approximation approaches and probabilistic functions are exceptionally strong in deciphering and responding to human voice inputs.

The n-gram, a continuous sequence of n elements from a given sample of text or voice, is now the foundation of computational linguistics.

Depending on the application, the objects might be pho nemes, syllables, letters, words, or base pairs.

N-grams are usually gathered from text or voice.

In terms of proficiency, no other method presently outperforms this one.

For their virtual assistants, Google and Bing have indexed the whole internet and incorporate user query data in their language models for voice search applications.

Today's systems are starting to identify new terms from their datasets on the fly, which is referred to as "lifelong learning" by humans, although this is still a novel technique.

Companies working in natural language processing will desire solutions that are portable (not reliant on distant servers), deliver near-instantaneous response, and provide a seamless user experience in the future.

Richard Socher, a deep learning specialist and the founder and CEO of the artificial intelligence start-up MetaMind, is working on a strong example of next-generation NLP.

Based on massive chunks of natural language information, the company's technology employs a neural networking architecture and reinforcement learning algorithms to provide responses to specific and highly broad inquiries.

Salesforce, the digital marketing powerhouse, just purchased the startup.

Text-to-speech analysis and advanced conversational interfaces in automobiles will be in high demand in the future, as will speech recognition and translation across cultures and languages, automatic speech understanding in noisy environments like construction sites, and specialized voice systems to control office and home automation processes and internet-connected devices.

To work on, any of these applications to enhance human speech will need the collection of massive data sets of natural language.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Natural Language Generation; Newell, Allen; Workplace Automation.

References & Further Reading:

Chowdhury, Gobinda G. 2003. “Natural Language Processing.” Annual Review of Information Science and Technology 37: 51–89.

Jurafsky, Daniel, and James H. Martin. 2014. Speech and Language Processing. Second edition. Upper Saddle River, NJ: Pearson Prentice Hall.

Mahavan, Radhika. n.d. “Natural Language Processing: Current Applications and Future Possibilities.” https://www.techemergence.com/nlp-current-applications-and-future-possibilities/.

Manning, Christopher D., and Hinrich Schütze. 1999. Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press.

Metz, Cade. 2015. “AI’s Next Frontier: Machines That Understand Language.” Wired, June 24, 2015. https://www.wired.com/2015/06/ais-next-frontier-machines-understand-language/.

Nusca, Andrew. 2011. “Say Command: How Speech Recognition Will Change the World.” 

ZDNet, November 2, 2011. https://www.zdnet.com/article/say-command-how-speech-recognition-will-change-the-world/.