Artificial Intelligence - Who Is Sherry Turkle?

 

 

 

Sherry Turkle(1948–) has a background in sociology and psychology, and her work focuses on the human-technology interaction.

While her study in the 1980s focused on how technology affects people's thinking, her work in the 2000s has become more critical of how technology is utilized at the expense of building and maintaining meaningful interpersonal connections.

She has employed artificial intelligence in products like children's toys and pets for the elderly to highlight what people lose out on when interacting with such things.

Turkle has been at the vanguard of AI breakthroughs as a professor at the Massachusetts Institute of Technology (MIT) and the creator of the MIT Initiative on Technology and the Self.

She highlights a conceptual change in the understanding of AI that occurs between the 1960s and 1980s in Life on the Screen: Identity inthe Age of the Internet (1995), substantially changing the way humans connect to and interact with AI.

She claims that early AI paradigms depended on extensive preprogramming and employed a rule-based concept of intelligence.

However, this viewpoint has given place to one that considers intelligence to be emergent.

This emergent paradigm, which became the recognized mainstream view by 1990, claims that AI arises from a much simpler set of learning algorithms.

The emergent method, according to Turkle, aims to emulate the way the human brain functions, assisting in the breaking down of barriers between computers and nature, and more generally between the natural and the artificial.

In summary, an emergent approach to AI allows people to connect to the technology more easily, even thinking of AI-based programs and gadgets as children.

Not just for the area of AI, but also for Turkle's study and writing on the subject, the rising acceptance of the emerging paradigm of AI and the enhanced relatability it heralds represents a significant turning point.

Turkle started to employ ethnographic research techniques to study the relationship between humans and their gadgets in two edited collections, Evocative Objects: Things We Think With (2007) and The Inner History of Devices (2008).

She emphasized in her book The Inner History of Devices that her intimate ethnography, or the ability to "listen with a third ear," is required to go past the advertising-based clichés that are often employed when addressing technology.

This method comprises setting up time for silent meditation so that participants may think thoroughly about their interactions with their equipment.

Turkle used similar intimate ethnographic approaches in her second major book, Alone Together: 

Why We Expect More from Technology and Less from Each Other (2011), to argue that the increasing connection between people and the technology they use is harmful.

These issues are connected to the increased usage of social media as a form of communication, as well as the continuous degree of familiarity and relatability with technology gadgets, which stems from the emerging AI paradigm that has become practically omnipresent.

She traced the origins of the dilemma back to early pioneers in the field of cybernetics, citing, for example, Norbert Weiner's speculations on the idea of transmitting a human person across a telegraph line in his book God & Golem, Inc.(1964).

Because it reduces both people and technology to information, this approach to cybernetic thinking blurs the barriers between them.

In terms of AI, this implies that it doesn't matter whether the machines with which we interact are really intelligent.

Turkle claims that by engaging with and caring for these technologies, we may deceive ourselves into feeling we are in a relationship, causing us to treat them as if they were sentient.

In a 2006 presentation titled "Artificial Intelligence at 50: From Building Intelligence to Nurturing Sociabilities" at the Dartmouth Artificial Intelligence Conference, she recognized this trend.

She identified the 1997 Tamagotchi, 1998 Furby, and 2000 MyReal Baby as early versions of what she refers to as relational artifacts, which are more broadly referred to as social machines in the literature.

The main difference between these devices and previous children's toys is that these devices come pre-animated and ready for a relationship, whereas previous children's toys required children to project a relationship onto them.

Turkle argues that this change is about our human weaknesses as much as it is about computer capabilities.

In other words, just caring for an item increases the likelihood of not only seeing it as intelligent but also feeling a connection to it.

This sense of connection is more relevant to the typical person engaging with these technologies than abstract philosophic considerations concerning the nature of their intelligence.

Turkle delves more into the ramifications of people engaging with AI-based technologies in both Alone Together and Reclaiming Conversation: The Power of Talk in a Digital Age (2015).

She provides the example of Adam in Alone Together, who appreciates the appreciation of the AI bots he controls over in the game Civilization.

Adam appreciates the fact that he is able to create something fresh when playing.

Turkle, on the other hand, is skeptical of this interaction, stating that Adam's playing isn't actual creation, but rather the sensation of creation, and that it's problematic since it lacks meaningful pressure or danger.

In Reclaiming Conversation, she expands on this point, suggesting that social partners simply provide a perception of camaraderie.

This is important because of the value of human connection and what may be lost in relationships that simply provide a sensation or perception of friendship rather than true friendship.

Turkle believes that this transition is critical.

She claims that although connections with AI-enabledtechnologies may have certain advantages, they pale in contrast to what is missing: 

• the complete complexity and inherent contradictions that define what it is to be human.

A person's connection with an AI-enabled technology is not as intricate as one's interaction with other individuals.

Turkle claims that as individuals have become more used to and dependent on technology gadgets, the definition of friendship has evolved.

• People's expectations for companionship have been simplified as a result of this transformation, and the advantages that one wants to obtain from partnerships have been reduced.

• People now tend to associate friendship only with the concept of interaction, ignoring the more nuanced sentiments and arguments that are typical in partnerships.

• By engaging with gadgets, one may form a relationship with them.

• Conversations between humans have become merely transactional as human communication has shifted away from face-to-face conversation and toward interaction mediated by devices. 

In other words, the most that can be anticipated is engagement.

Turkle, who has a background in psychoanalysis, claims that this kind of transactional communication allows users to spend less time learning to view the world through the eyes of another person, which is a crucial ability for empathy.

Turkle argues we are in a robotic period in which people yearn for, and in some circumstances prefer, AI-based robotic companionship over that of other humans, drawing together these numerous streams of argument.

For example, some people enjoy conversing with their iPhone's Siri virtual assistant because they aren't afraid of being judged by it, as evidenced by a series of Siri commercials featuring celebrities talking to their phones.

Turkle has a problem with this because these devices can only respond as if they understand what is being said.

AI-based gadgets, on the other hand, are confined to comprehending the literal meanings of data stored on the device.

They can decipher the contents of phone calendars and emails, but they have no idea what any of this data means to the user.

There is no discernible difference between a calendar appointment for car maintenance and one for chemotherapy for an AI-based device.

A person may lose sight of what it is to have an authentic dialogue with another human when entangled in a variety of these robotic connections with a growing number of technologies.

While Reclaiming Communication documents deteriorating conversation skills and decreasing empathy, it ultimately ends on a positive note.

Because people are becoming increasingly dissatisfied with their relationships, there may be a chance for face-to-face human communication to reclaim its vital role.

Turkle's ideas focus on reducing the amount of time people spend on their phones, but AI's involvement in this interaction is equally critical.

• Users must accept that their virtual assistant connections will never be able to replace face-to-face interactions.

• This will necessitate being more deliberate in how one uses devices, prioritizing in-person interactions over the faster and easier interactions provided by AI-enabled devices.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Blade Runner; Chatbots and Loebner Prize; ELIZA; General and Narrow AI; Moral Turing Test; PARRY; Turing, Alan; 2001: A Space Odyssey.

References And Further Reading

• Haugeland, John. 1997. “What Is Mind Design?” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 1–28. Cambridge, MA: MIT Press.

• Searle, John R. 1997. “Minds, Brains, and Programs.” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 183–204. Cambridge, MA: MIT Press.

• Turing, A. M. 1997. “Computing Machinery and Intelligence.” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 29–56. Cambridge, MA: MIT Press.

AI - What Is Superintelligence AI? Is Artificial Superintelligence Possible?

 

 

In its most common use, the phrase "superintelligence" refers to any degree of intelligence that at least equals, if not always exceeds, human intellect, in a broad sense.

Though computer intelligence has long outperformed natural human cognitive capacity in specific tasks—for example, a calculator's ability to swiftly interpret algorithms—these are not often considered examples of superintelligence in the strict sense due to their limited functional range.

In this sense, superintelligence would necessitate, in addition to artificial mastery of specific theoretical tasks, some kind of additional mastery of what has traditionally been referred to as practical intelligence: a generalized sense of how to subsume particulars into universal categories that are in some way worthwhile.

To this day, no such generalized superintelligence has manifested, and hence all discussions of superintelligence remain speculative to some degree.

Whereas traditional theories of superintelligence have been limited to theoretical metaphysics and theology, recent advancements in computer science and biotechnology have opened up the prospect of superintelligence being materialized.

Although the timing of such evolution is hotly discussed, a rising body of evidence implies that material superintelligence is both possible and likely.

If this hypothesis is proved right, it will very certainly be the result of advances in one of two major areas of AI research: 

1. Bioengineering 
2. Computer science

The former involves efforts to not only map out and manipulate the human DNA, but also to exactly copy the human brain electronically through full brain emulation, also known as mind uploading.

The first of these bioengineering efforts is not new, with eugenics programs reaching back to the seventeenth century at the very least.

Despite the major ethical and legal issues that always emerge as a result of such efforts, the discovery of DNA in the twentieth century, together with advances in genome mapping, has rekindled interest in eugenics.

Much of this study is aimed at gaining a better understanding of the human brain's genetic composition in order to manipulate DNA code in the direction of superhuman intelligence.

Uploading is a somewhat different, but still biologically based, approach to superintelligence that aims to map out neural networks in order to successfully transfer human intelligence onto computer interfaces.

• The brains of insects and tiny animals are micro-dissected and then scanned for thorough computer analysis in this relatively new area of study.

• The underlying premise of whole brain emulation is that if the brain's structure is better known and mapped, it may be able to copy it with or without organic brain tissue.

Despite the fast growth of both genetic mapping and whole brain emulation, both techniques have significant limits, making it less likely that any of these biological approaches will be the first to attain superintelligence.

The genetic alteration of the human genome, for example, is constrained by generational constraints.

Even if it were now feasible to artificially boost cognitive functioning by modifying the DNA of a human embryo (which is still a long way off), it would take an entire generation for the changed embryo to evolve into a fully fledged, superintelligent human person.

This would also imply that there are no legal or moral barriers to manipulating the human DNA, which is far from the fact.

Even the comparatively minor genetic manipulation of human embryos carried done by a Chinese physician as recently as November 2018 sparked international outrage (Ramzy and Wee 2019).

Whole brain emulation, on the other hand, is still a long way off, owing to biotechnology's limits.

Given the current medical technology, the extreme levels of accuracy necessary at every step of the uploading process are impossible to achieve.

Science and technology currently lack the capacity to dissect and scan human brain tissue with sufficient precision to produce full brain simulation results.

Furthermore, even if such first steps are feasible, researchers would face significant challenges in analyzing and digitally replicating the human brain using cutting-edge computer technology.

Many analysts believe that such constraints will be overcome, although the timeline for such realizations is unknown.

Apart from biotechnology, the area of AI, which is strictly defined as any type of nonorganic (particularly computer-based) intelligence, is the second major path to superintelligence.

Of course, the work of creating a superintelligent AI from the ground up is complicated by a number of elements, not all of which are purely logistical in nature, such as processing speed, hardware/software design, finance, and so on.

In addition to such practical challenges, there is a significant philosophical issue: human programmers are unable to know, and so cannot program, that which is superior to their own intelligence.

Much contemporary research on computer learning and interest in the notion of a seed AI is motivated in part by this worry.

Any machine capable of changing reactions to stimuli based on an examination of how well it performs in relation to a predetermined objective is defined as the latter.

Importantly, the concept of a seed AI entails not only the capacity to change its replies by extending its base of content knowledge (stored information), but also the ability to change the structure of its programming to better fit a specific job (Bostrom 2017, 29).

Indeed, it is this latter capability that would give a seed AI what Nick Bostrom refers to as "recursive self-improvement," or the ability to evolve iteratively (Bostrom 2017, 29).

This would eliminate the requirement for programmers to have an a priori vision of super intelligence since the seed AI would constantly enhance its own programming, with each more intelligent iteration writing a superior version of itself (beyond the human level).

Such a machine would undoubtedly cast doubt on the conventional philosophical assumption that robots are incapable of self-awareness.

This perspective's proponents may be traced all the way back to Descartes, but they also include more current thinkers like John Haugeland and John Searle.

Machine intelligence, in this perspective, is defined as the successful correlation of inputs with outputs according to a predefined program.

As a result, robots differ from humans in type, the latter being characterized only by conscious self-awareness.

Humans are supposed to comprehend the activities they execute, but robots are thought to carry out functions mindlessly—that is, without knowing how they work.

Should it be able to construct a successful seed AI, this core idea would be forced to be challenged.

The seed AI would demonstrate a level of self-awareness and autonomy not readily explained by the Cartesian philosophical paradigm by upgrading its own programming in ways that surprise and defy the forecasts of its human programmers.

Indeed, although it is still speculative (for the time being), the increasingly possible result of superintelligent AI poses a slew of moral and legal dilemmas that have sparked a lot of philosophical discussion in this subject.

The main worries are about the human species' security in the case of what Bostrom refers to as a "intelligence explosion"—that is, the creation of a seed AI followed by a possibly exponential growth in intellect (Bostrom 2017).

One of the key problems is the inherently unexpected character of such a result.

Humans will not be able to totally foresee how superintelligent AI would act due to the autonomy entailed by superintelligence in a definitional sense.

Even in the few cases of specialized superintelligence that humans have been able to construct and study so far—for example, robots that have surpassed humans in strategic games like chess and Go—human forecasts for AI have shown to be very unreliable.

For many critics, such unpredictability is a significant indicator that, should more generic types of superintelligent AI emerge, humans would swiftly lose their capacity to manage them (Kissinger 2018).

Of all, such a loss of control does not automatically imply an adversarial relationship between humans and superintelligence.

Indeed, although most of the literature on superintelligence portrays this relationship as adversarial, some new work claims that this perspective reveals a prejudice against machines that is particularly prevalent in Western cultures (Knight 2014).

Nonetheless, there are compelling grounds to believe that superintelligent AI would at the very least consider human goals as incompatible with their own, and may even regard humans as existential dangers.

For example, computer scientist Steve Omohundro has claimed that even a relatively basic kind of superintelligent AI like a chess bot would have motive to want the extinction of humanity as a whole—and may be able to build the tools to do it (Omohundro 2014).

Similarly, Bostrom has claimed that a superintelligence explosion would most certainly result in, if not the extinction of the human race, then at the very least a gloomy future (Bostrom 2017).

Whatever the benefits of such theories, the great uncertainty entailed by superintelligence is obvious.

If there is one point of agreement in this large and diverse literature, it is that if AI research is to continue, the global community must take great care to protect its interests.

Hardened determinists who claim that technological advancement is so tightly connected to inflexible market forces that it is simply impossible to change its pace or direction in any major manner may find this statement contentious.

According to this determinist viewpoint, if AI can deliver cost-cutting solutions for industry and commerce (as it has already started to do), its growth will proceed into the realm of superintelligence, regardless of any unexpected negative repercussions.

Many skeptics argue that growing societal awareness of the potential risks of AI, as well as thorough political monitoring of its development, are necessary counterpoints to such viewpoints.

Bostrom highlights various examples of effective worldwide cooperation in science and technology as crucial precedents that challenge the determinist approach, including CERN, the Human Genome Project, and the International Space Station (Bostrom 2017, 253).

To this, one may add examples from the worldwide environmental movement, which began in the 1960s and 1970s and has imposed significant restrictions on pollution committed in the name of uncontrolled capitalism (Feenberg 2006).

Given the speculative nature of superintelligence research, it is hard to predict what the future holds.

However, if superintelligence poses an existential danger to human existence, caution would dictate that a worldwide collaborative strategy rather than a free market approach to AI be used.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Berserkers; Bostrom, Nick; de Garis, Hugo; General and Narrow AI; Goertzel, Ben; Kurzweil, Ray; Moravec, Hans; Musk, Elon; Technological Singularity; Yudkowsky, Eliezer.

References & Further Reading:

• Bostrom, Nick. 2017. Superintelligence: Paths, Dangers, Strategies. Oxford, UK: Oxford University Press.

• Feenberg, Andrew. 2006. “Environmentalism and the Politics of Technology.” In Questioning Technology, 45–73. New York: Routledge.

• Kissinger, Henry. 2018. “How the Enlightenment Ends.” The Atlantic, June 2018. https://www.theatlantic.com/magazine/archive/2018/06/henry-kissinger-ai-could-mean-the-end-of-human-history/559124/.

• Knight, Heather. 2014. How Humans Respond to Robots: Building Public Policy Through Good Design. Washington, DC: The Project on Civilian Robotics. Brookings Institution.

• Omohundro, Steve. 2014. “Autonomous Technology and the Greater Human Good.” Journal of Experimental & Theoretical Artificial Intelligence 26, no. 3: 303–15.

• Ramzy, Austin, and Sui-Lee Wee. 2019. “Scientist Who Edited Babies’ Genes Is Likely to Face Charges in China.” The New York Times, January 21, 2019

Artificial Intelligence - Autonomy And Complacency In AI Systems.

 

The concepts of machine autonomy and human autonomy and complacency are intertwined.

Artificial intelligences are undoubtedly getting more independent as they are trained to learn from their own experiences and data intake.

Machines that gain more skills than humans tend to become increasingly dependent on them to make judgments and react correctly to unexpected events.

This dependence on AI systems' decision-making processes might lead to a loss of human agency and complacency.

This complacency may result in the AI's system or decision-making processes failing to respond to major faults.

Autonomous machines are ones that can function in unsupervised settings, adapt to new situations and experiences, learn from previous errors, and decide the best potential outcomes in each case without the need for fresh programming input.

To put it another way, these robots learn from their experiences and are capable of going beyond their original programming in certain respects.

The concept is that programmers won't be able to foresee every circumstance that an AI-enabled machine could experience based on its activities, thus it must be able to adapt.

This is not widely recognized, since others say that these systems' adaptability is inherent in their programming, as their programs are designed to be adaptable.

The disagreement over whether any agent, including humans, can express free will and act autonomously exacerbates these debates.

With the advancement of technology, the autonomy of AI programs is not the only element of autonomy that is being explored.

Worries have also been raised concerning the influence on human autonomy, as well as concerns about machine complacency.

People who gain from the machine's choice being irrelevant since they no longer have to make decisions as AI systems grow increasingly tuned to anticipate people's wishes and preferences.

The interaction of human employees and automated systems has gotten a lot of attention.

According to studies, humans are more prone to overlook flaws in these procedures, particularly when they get routinized, which leads to a positive expectation of success rather than a negative expectation of failure.

This sense of accomplishment causes the operators or supervisors of automated processes to place their confidence in inaccurate readouts or machine judgments, which may lead to mistakes and accidents.

~ Jai Krishna Ponnappan

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

See also: 

Accidents and Risk Assessment; Autonomous and Semiautonomous Systems.

Further Reading

André, Quentin, Ziv Carmon, Klaus Wertenbroch, Alia Crum, Frank Douglas, William 

Goldstein, Joel Huber, Leaf Van Boven, Bernd Weber, and Haiyang Yang. 2018. 

“Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data.” Customer Needs and Solutions 5, no. 1–2: 28–37.

Bahner, J. Elin, Anke-Dorothea Hüper, and Dietrich Manzey. 2008. “Misuse of Auto￾mated Decision Aids: Complacency, Automation Bias, and the Impact of Training 

Experience.” International Journal of Human-Computer Studies 66, no. 9: 

688–99.

Lawless, W. F., Ranjeev Mittu, Donald Sofge, and Stephen Russell, eds. 2017. Autonomy 

and Intelligence: A Threat or Savior? Cham, Switzerland: Springer.

Parasuraman, Raja, and Dietrich H. Manzey. 2010. “Complacency and Bias in Human 

Use of Automation: An Attentional Integration.” Human Factors 52, no. 3: 

381–410.

Optical Computing Systems To Speed Up AI And Machine Learning.

Artificial intelligence and machine learning are influencing our lives in a variety of minor but significant ways right now. 

For example, AI and machine learning programs propose content from streaming services like Netflix and Spotify that we would appreciate. 

These technologies are expected to have an even greater influence on society in the near future, via activities such as driving completely driverless cars, allowing sophisticated scientific research, and aiding medical breakthroughs. 

However, the computers that are utilized for AI and machine learning use a lot of power. 

The need for computer power associated with these technologies is now doubling every three to four months. 

Furthermore, cloud computing data centers employed by AI and machine learning applications use more electricity each year than certain small nations. 

It's clear that this level of energy usage cannot be sustained. 

A research team lead by the University of Washington has created new optical computing hardware for AI and machine learning that is far quicker and uses much less energy than traditional electronics. 

Another issue addressed in the study is the 'noise' inherent in optical computing, which may obstruct computation accuracy. 

The team showcases an optical computing system for AI and machine learning in a new research published Jan. 

Science Advances that not only mitigates noise but also utilizes part of it as input to assist increase the creative output of the artificial neural network inside the system. 

Changming Wu, a UW doctorate student in electrical and computer engineering, stated, "We've constructed an optical computer that is quicker than a typical digital computer." 

"Moreover, this optical computer can develop new objects based on random inputs provided by optical noise, which most researchers have attempted to avoid." 

Optical computing noise is primarily caused by stray light particles, or photons, produced by the functioning of lasers inside the device as well as background heat radiation. 

To combat noise, the researchers linked their optical computing core to a Generative Adversarial Network, a sort of machine learning network. 

The researchers experimented with a variety of noise reduction strategies, including utilizing part of the noise created by the optical computing core as random inputs for the GAN. 

The researchers, for example, gave the GAN the job of learning how to handwrite the number "7" in a human-like manner. 

The number could not simply be printed in a predetermined typeface on the optical computer. 

It had to learn the task in the same way that a kid would, by studying visual examples of handwriting and practicing until it could accurately write the number. 

Because the optical computer lacked a human hand for writing, its "handwriting" consisted of creating digital pictures with a style close to but not identical to the examples it had examined. 

"Instead of teaching the network to read handwritten numbers, we taught it to write numbers using visual examples of handwriting," said senior author Mo Li, an electrical and computer engineering professor at the University of Washington. 

"We also demonstrated that the GAN can alleviate the detrimental effect of optical computing hardware sounds by utilizing a training technique that is resilient to mistakes and noises, with the support of our Duke University computer science teammates. 

Furthermore, the network treats the sounds as random input, which is required for the network to create output instances." 

The GAN practiced writing "7" until it could do it effectively after learning from handwritten examples of the number seven from a normal AI-training picture collection. 

It developed its own writing style along the way and could write numbers from one to ten in computer simulations. 

The next stage will be to scale up the gadget using existing semiconductor manufacturing methods. 

To attain wafer-scale technology, the team wants to employ an industrial semiconductor foundry rather than build the next iteration of the device in a lab. 

A larger-scale gadget will boost performance even further, allowing the study team to undertake more sophisticated activities such as making artwork and even films in addition to handwriting production. 

"This optical system represents a computer hardware architecture that can enhance the creativity of artificial neural networks used in AI and machine learning," Li explained. 

"More importantly, it demonstrates the viability of this system at a large scale where noise and errors can be mitigated and even harnessed." "AI applications are using so much energy that it will be unsustainable in the future. 

This technique has the potential to minimize energy usage, making AI and machine learning more environmentally friendly—as well as incredibly quick, resulting in greater overall performance." Although many people are unaware of it, artificial intelligence (AI) and machine learning are now a part of our regular life online. 

Intelligent ranking algorithms, for example, help search engines like Google, video streaming services like Netflix utilize machine learning to customize movie suggestions, and cloud computing data centers employ AI and machine learning to help with a variety of services. 

The requirements for AI are many, diverse, and difficult. 

As these needs rise, so does the need to improve AI performance while also lowering its energy usage. 

The energy costs involved with AI and machine learning on a broad scale may be startling. 

Cloud computing data centers, for example, use an estimated 200 terawatt hours per year — enough energy to power a small nation — and this consumption is expected to expand enormously in the future years, posing major environmental risks. 

Now, a team lead by associate professor Mo Li of the University of Washington Department of Electrical and Computer Engineering (UW ECE) has developed a method in partnership with academics from the University of Maryland that might help speed up AI while lowering energy and environmental expenses. 

The researchers detailed an optical computing core prototype that employs phase-change material in a publication published in Nature Communications on January 4, 2021. 

(a substance similar to what CD-ROMs and DVDs use to record information). 

Their method is quick, energy-efficient, and capable of speeding up AI and machine learning neural networks. 

The technique is also scalable and immediately relevant to cloud computing, which employs AI and machine learning to power common software applications like search engines, streaming video, and a plethora of apps for phones, desktop computers, and other devices. 

"The technology we designed is geared to execute artificial neural network algorithms, which are a backbone method for AI and machine learning," Li said. 

"This breakthrough in research will make AI centers and cloud computing significantly more energy efficient and speedier." 

The team is one of the first in the world to employ phase-change material in optical computing to allow artificial neural networks to recognize images. 

Recognizing a picture in a photo is simple for humans, but it requires a lot of computing power for AI. 

Image recognition is a benchmark test of a neural network's computational speed and accuracy since it requires a lot of computation. 

This test was readily passed by the team's optical computing core, which was running an artificial neural network. 

"Optical computing initially surfaced as a concept in the 1980s, but it eventually died in the shadow of microelectronics," said Changming Wu, a graduate student in Li's group. 

"It has now been updated due to the end of Moore's law [the discovery that the number of transistors in a dense, integrated circuit doubles every two years], developments in integrated photonics, and the needs of AI computing."

 That's a lot of fun." Optical computing is quick because it transmits data at incredible rates using light created by lasers rather than the considerably slower electricity utilized in typical digital electronics. 

The prototype built by the study team was created to speed up the computational speed of an artificial neural network, which is measured in billions and trillions of operations per second. 

Future incarnations of their technology, according to Li, have the potential to move much quicker. 

"This is a prototype, and we're not utilizing the greatest speed possible with optics just yet," Li said. 

"Future generations have the potential to accelerate by at least an order of magnitude." Any program powered by optical computing over the cloud — such as search engines, video streaming, and cloud-enabled gadgets — will operate quicker, enhancing performance, in the ultimate real-world use of this technology. 

Li's research team took their prototype a step further by sensing light emitted via phase-change material to store data and conduct computer operations. 

Unlike transistors in digital electronics, which need a constant voltage to represent and maintain the zeros and ones required in binary computing, phase-change material does not require any energy. 

When phase-change material is heated precisely by lasers, it shifts between a crystalline and an amorphous state, much like a CD or DVD. 

The material then retains that condition, or "phase," as well as the information that phase conveys (a zero or one), until the laser heats it again. 

"There are other competing schemes to construct optical neural networks," Li explained, "but we believe that using phase-changing material has a unique advantage in terms of energy efficiency because the data is encoding in a non-volatile way, meaning that the device does not consume a constant amount of power to store the data." 

"Once the info is written there, it stays there indefinitely." You don't need to provide electricity to keep it in place." 

This energy savings is important because it is multiplied by millions of computer servers in hundreds of data centers throughout the globe, resulting in a huge decrease in energy consumption and environmental effect. 

By patterning the phase-change material used in their optical computing core into nanostructures, the team was able to improve it even further. 

These tiny structures increase the material's durability and stability, as well as its contrast (the ability to discriminate between zero and one in binary code) and computing capacity and accuracy. 

The optical computer core of the prototype was also completely integrated with phase-change material, thanks to Li's research team. 

"We're doing all we can to incorporate optics here," Wu said. 

"We layer the phase-change material on top of a waveguide, which is a thin wire that we cut into the silicon chip to channel light. 

You may conceive of it as a light-emitting electrical wire or an optical fiber etched into the chip." 

Li's research group claims that the technology they created is one of the most scalable methods to optical computing technologies now available, with the potential to be used to massive systems like networked cloud computing servers in data centers across the globe. 

"Our design architecture is scalable to a much, much bigger network," Li added, "and can tackle hard artificial intelligence tasks ranging from massive, high-resolution image identification to video processing and video image recognition."

"We feel our system is the most promising and scalable to that degree." 

Of course, this will need large-scale semiconductor production. 

Our design and the prototype's substance are both extremely compatible with semiconductor foundry procedures."

Looking forward, Li said he could see optical computing devices like the one his team produced boosting current technology's processing capacity and allowing the next generation of artificial intelligence. 

To take the next step in that direction, his research team will collaborate closely with UW ECE associate professor Arka Majumdar and assistant professor Sajjad Moazeni, both specialists in large-scale integrated photonics and microelectronics, to scale up the prototype they constructed. 

And, once the technology has been scaled up enough, it will lend itself to future integration with energy-intensive data centers, speeding up the performance of cloud-based software applications while lowering energy consumption. 

"The computers in today's data centers are already linked via optical fibers. 

This enables ultra-high bandwidth transmission, which is critical," Li said. 

"Because fiber optics infrastructure is already in place, it's reasonable to do optical computing in such a setup." It's fantastic, and I believe the moment has come for optical computing to resurface."

~ Jai Krishna Ponnappan

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

See also: 

Optical Computing, Optical Computing Core, AI, Machine Learning, AI Systems.

Further Reading:

Changming Wu et al, Harnessing optoelectronic noises in a photonic generative network, Science Advances (2022). DOI: 10.1126/sciadv.abm2956. www.science.org/doi/10.1126/sciadv.abm2956