Artificial Intelligence

ARTIFICIAL INTELLIGENCE

We’re still a few years away from having robots at our beck and call, but AI has already had a profound impact in more subtle ways. Weather forecasts, email spam filtering, Google’s search predictions, and voice recognition, such Apple’s Siri, are all examples. What these technologies have in common are machine-learning algorithms that enable them to react and respond in real time. There will be growing pains as AI technology evolves, but the positive effect it will have on society in terms of efficiency is immeasurable.

Introduction

·        Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal. 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" (known as Machine Learning). 

·        As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition. For instance, optical character recognition is no longer perceived as an example of "artificial intelligence", having become a routine technology.Capabilities currently classified as AI include successfully understanding human speech, competing at a high level in strategic game systems (such as Chess and Go), self-driving cars, intelligent routing in content delivery networks, and interpreting complex data.

·        AI research is divided into subfields that focus on specific problems or on specific approaches or on the use of a particular tool or towards satisfying particular applications.

·        The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.

·         General intelligence is among the field's long-term goals.Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics.

·        The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience and artificial psychology.

·        The field was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it". This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity.

·        Some people also consider AI a danger to humanity if it progresses unabatedly. Attempts to create artificial intelligence have experienced many setbacks, including the ALPAC report of 1966, the abandonment of perceptrons in 1970, the Lighthill Report of 1973, the second AI winter 1987–1993 and the collapse of the Lisp machine market in 1987.

·        In the twenty-first century, AI techniques, both "hard" and "soft" have experienced a resurgence following concurrent advances in computer power, sizes of training sets, and theoretical understanding, and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science.

WHY RESEARCH AI SAFETY?

In the near term, the goal of keeping AI’s impact on society beneficial motivates research in many areas, from economics and law to technical topics such as verification, validity, security and control. Whereas it may be little more than a minor nuisance if your laptop crashes or gets hacked, it becomes all the more important that an AI system does what you want it to do if it controls your car, your airplane, your pacemaker, your automated trading system or your power grid. Another short-term challenge is preventing a devastating arms race in lethal autonomous weapons.

In the long term, an important question is what will happen if the quest for strong AI succeeds and an AI system becomes better than humans at all cognitive tasks. As pointed out by I.J. Good in 1965, designing smarter AI systems is itself a cognitive task. Such a system could potentially undergo recursive self-improvement, triggering an intelligence explosion leaving human intellect far behind. By inventing revolutionary new technologies, such a superintelligence might help us eradicate war, disease, and poverty, and so the creation of strong AI might be the biggest event in human history. Some experts have expressed concern, though, that it might also be the last, unless we learn to align the goals of the AI with ours before it becomes super-intelligent.

There are some who question whether strong AI will ever be achieved, and others who insist that the creation of super-intelligent AI is guaranteed to be beneficial. At FLI we recognize both of these possibilities, but also recognize the potential for an artificial intelligence system to intentionally or unintentionally cause great harm. We believe research today will help us better prepare for and prevent such potentially negative consequences in the future, thus enjoying the benefits of AI while avoiding pitfalls.

HOW CAN AI BE DANGEROUS?

Most researchers agree that a super-intelligent AI is unlikely to exhibit human emotions like love or hate, and that there is no reason to expect AI to become intentionally benevolent or malevolent. Instead, when considering how AI might become a risk, experts think two scenarios most likely:

1. The AI is programmed to do something devastating: Autonomous weapons are artificial intelligence systems that are programmed to kill. In the hands of the wrong person, these weapons could easily cause mass casualties. Moreover, an AI arms race could inadvertently lead to an AI war that also results in mass casualties. To avoid being thwarted by the enemy, these weapons would be designed to be extremely difficult to simply “turn off,” so humans could plausibly lose control of such a situation. This risk is one that’s present even with narrow AI, but grows as levels of AI intelligence and autonomy increase.

2. The AI is programmed to do something beneficial, but it develops a destructive method for achieving its goal: This can happen whenever we fail to fully align the AI’s goals with ours, which is strikingly difficult. If you ask an obedient intelligent car to take you to the airport as fast as possible, it might get you there chased by helicopters and covered in vomit, doing not what you wanted but literally what you asked for. If a super-intelligent system is tasked with an ambitious geoengineering project, it might wreak havoc with our ecosystem as a side effect, and view human attempts to stop it as a threat to be met.

As these examples illustrate, the concern about advanced AI isn’t malevolence but competence. A super-intelligent AI will be extremely good at accomplishing its goals, and if those goals aren’t aligned with ours, we have a problem. You’re probably not an evil ant-hater who steps on ants out of malice, but if you’re in charge of a hydroelectric green energy project and there’s an anthill in the region to be flooded, too bad for the ants. A key goal of AI safety research is to never place humanity in the position of those ants.

Goals of AI

·        To Create Expert Systems − The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.

·        To Implement Human Intelligence in Machines − Creating systems that understand, think, learn, and behave like humans.

What Contributes to AI?

Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major thrust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning, and problem solving.

Out of the following areas, one or multiple areas can contribute to build an intelligent system.

THE BUSINESS EFFECT

Nowhere has AI had a greater impact in the early stages of the 21st century than in the office. Machine-learning technologies are driving increases in productivity never before seen. From workflow management tools to trend predictions and even the way brands purchase advertising, AI is changing the way we do business. In fact, a Japanese venture capital firm recently became the first company in history to nominate an AI board member for its ability to predict market trends faster than humans.

Big data is a goldmine for businesses, but companies are practically drowning in it. Yet, it’s been a primary driver for AI advancements, as machine-learning technologies can collect and organize massive amounts of information to make predictions and insights that are far beyond the capabilities of manual processing. Not only does it increase organizational efficiency, but it dramatically reduces the likelihood that a critical mistake will be made. AI can detect irregular patterns, such as spam filtering or payment fraud, and alert businesses in real time about suspicious activities. Businesses can “train” AI machines to handle incoming customer support calls, reducing costs. It can even be used to optimize the sales funnel by scanning the database and searching the Web for prospects that exhibit the same buying patterns as existing customers.

There is so much potential for AI development that it’s getting harder to imagine a future without it. We’re already seeing an increase in workplace productivity thanks to AI advancements. By the end of the decade, AI will become commonplace in everyday life, whether it’s self-driving cars, more accurate weather predictions, or space exploration. We will even see machine-learning algorithms used to prevent cyberterrorism and payment fraud, albeit with increasing public debate over privacy implications. AI will also have a strong impact in healthcare advancements due to its ability to analyze massive amounts of genomic data, leading to more accurate prevention and treatment of medical conditions on a personalized level.

But don’t expect a machine takeover any time soon. As easy as it is for machine-learning technology to self-improve, what it lacks is intuition. There’s a gut instinct that can’t be replicated via algorithms, making humans an important piece of the puzzle. The best way forward is for humans and machines to live harmoniously, leaning on one another’s strengths. Advertising is a perfect example, where machines are now doing much of the purchasing through programmatic exchanges to maximize returns on investment, allowing advertisers to focus on creating more engaging content.

While early science fiction writers might have expected more from AI at this stage, the rest of the world is generally satisfied with our progress. After all, not everyone is ready for humanoid robots or self-learning spaceships.

Applications of AI

AI has been dominant in various fields such as −

·        Gaming − AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge.

·        Natural Language Processing − It is possible to interact with the computer that understands natural language spoken by humans.

·        Expert Systems − There are some applications which integrate machine, software, and special information to impart reasoning and advising. They provide explanation and advice to the users.

·        Vision Systems − These systems understand, interpret, and comprehend visual input on the computer. For example,

o   A spying aeroplane takes photographs, which are used to figure out spatial information or map of the areas.

o   Doctors use clinical expert system to diagnose the patient.

o   Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist.

·        Speech Recognition − Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in human’s noise due to cold, etc.

·        Handwriting Recognition − The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text.

·        Intelligent Robots − Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment.

CONCLUSION

AI is at the centre of a new enterprise to build computational models of intelligence. The main assumption is that intelligence (human or otherwise) can be represented in terms of symbol structures and symbolic operations which can be programmed in a digital computer. There is much debate as to whether such an appropriately programmed computer would be a mind, or would merely simulate one, but AI researchers need not wait for the conclusion to that debate, nor for the hypothetical computer that could model all of human intelligence. Aspects of intelligent behaviour, such as solving problems, making inferences, learning, and understanding language, have already been coded as computer programs, and within very limited domains, such as identifying diseases of soybean plants, AI programs can outperform human experts. Now the great challenge of AI is to find ways of representing the commonsense knowledge and experience that enable people to carry out everyday activities such as holding a wide-ranging conversation, or finding their way along a busy street.