An Introduction to Artificial Intelligence, Lecture notes of Artificial Intelligence

Download An Introduction to Artificial Intelligence and more Lecture notes Artificial Intelligence in PDF only on Docsity! AN INTRODUCTION TO ARTIFICIAL INTELLIGENCE COMPILED BY HOWIE BAUM 1 2 • Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals, such as "learning" and "problem solving. .  In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Ways that People Think and Learn About Things • If you have a problem, think of a past situation where you solved a similar problem. • If you take an action, anticipate what might happen next. • If you fail at something, imagine how you might have done things differently. • If you observe an event, try to infer what prior event might have caused it. • If you see an object, wonder if anyone owns it. • If someone does something, ask yourself what the person's purpose was in doing that. 5 This is what Humans do best Can you list the items in this picture ? A computer might have trouble identifying the cat there. Can you count the distribution of letters in a book? Add a thousand 4-digit numbers? Match finger prints? Search a list of a million values for duplicates? This is what Computers do best Artificial intelligence (AI) - The study of computer systems that attempt to model and apply the intelligence of the human mind. For example, writing a program to pick out objects in a picture: 6 When we compare Humans to Machines, it is important to note that a Machine can be a car, a Smart Phone, a Digital Television, etc. 7 Figure 1. Anew mind-set for the no-collar workforce Humans and machines can develop a symbiotic relationship, each with specialized skills and abilities, in a unified workforce that delivers multifaceted benefits to the business. © Psychomotor, sensory, physical | @ Cognitive JERE © Content, process, system | @ Social | HUMANS Speech clarity Near vision Fine manual dexterity — Complex problem-solving pose ----- MACHINES N. x = ag! : Perception 7 Coordination t ' Precision Rate control te ' . s 7 Strength Basic speech : , ' Sound localization ' nea G A Speech recognition INTELLIGENT S Dynamic flexibility AUTOMATION _ a Night & peripheral vision 4 Reactiontime Stamina Regular object manipulation ENHANCEDROLE =a -- SPECIALIZATION Scalable processing capacity f = IMPROVED tenet anesnnse on oe Empathy Persuasion Emotional intelligence Social perceptiveness Negotiation DECISION-MAKING INCREASED PRODUCTIVITY, INNOVATION, EFFICIENCY Condition monitoring Data discovery 10 KEY RESEARCH AREAS IN AI • Problem solving, planning, and search --- generic problem solving architecture based on ideas from cognitive science (game playing, robotics). • Knowledge Representation – to store and manipulate information (logical and probabilistic representations) • Automated reasoning / Inference – to use the stored information to answer questions and draw new conclusions • Machine Learning – intelligence from data; to adapt to new circumstances and to detect and extrapolate patterns • Natural Language Processing – to communicate with the machine • Computer Vision --- processing visual information • Robotics --- Autonomy, manipulation, full integration of AI capabilities 11 From SIRI and Alexa, to self-driving cars, artificial intelligence (AI) is progressing rapidly. While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google’s search algorithms, to IBM’s Watson, to autonomous weapons. Artificial intelligence today is properly known as narrow AI (or weak AI), in that it is designed to perform a narrow task such as only facial recognition, or only internet searches, or only driving a car). However, the long-term goal of many researchers is to create general AI (AGI or strong AI). While narrow AI may outperform humans at whatever its specific task is, like playing chess or solving equations, AGI would outperform humans at nearly every thinking task. 12 AUTOMATONS – ARE THESE DEVICES INTELLIGENT ? https://www.youtube.com/watch?v=C7oSFNKIlaM (2.22 min) 15 16 Artificial Intelligence (AI) has entered our daily lives like never before and we are yet to unravel the many other ways in which it could flourish. All of the tech giants such as Microsoft, Uber, Google, Facebook, Apple, Amazon, Oracle, Intel, IBM or Twitter are competing in the race to lead the market and acquire the most innovative and promising AI businesses. SP aS 1950 fect ements PAPI tefl oe feng ay intelligence. 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The technology is directed towards completing specific tasks, such as scheduling certain types of appointments. For such tasks, the system makes the conversational experience as natural as possible, allowing people to speak normally, like they would to another person, without having to adapt to a machine. Neural network tracks treatment of brain tumors on MRI Physicians and scientists in Germany have developed an artificial neural network that’s capable of interpreting brain MRI scans to tell neuroradiologists how brain tumors are responding to chemotherapy and radiation therapy, according to a study published in The Lancet Oncology. Be Bach in the first Al-powered Google Doodle https://www.youtube.com/watch?v=gsUVOmMGEGaY 22 25 The answer is all of the above. Each of these highly realistic images were created by generative adversarial networks, or GANs. GAN, a concept introduced by Google researcher Ian Goodfellow in 2014, taps into the idea of “AI versus AI.” There are two neural networks: the generator, which comes up with a fake image (say a dog for instance), and a discriminator, which compares the result to real-world images and gives feedback to the generator on how close it is to replicating a realistic image. Researchers at CMU used GANSs for “face-to-face” translation in this iteration of “deepfake” videos. In the deepfake example below, John Oliver turns into Stephen Colbert: Input Output Source: tap //name cecree ecu~aepechbyRerycleGan, 26 Art auction house Christie's sold its first ever GAN-generated painting for a whopping $432,500. eT ap ee i ee ee oe ieee Poctust of Euirced Balany 2018 crented by GAN (Generative Acversana! Nebeos 30 In the Turing test, the interrogator must determine which respondent is the computer and which is the human. 31 THE LOEBNER PRIZE FOR COMPLETING THE TURING TEST The Loebner Prize is an annual competition in artificial intelligence that awards prizes to the computer programs considered by the judges to be the most human- like, using the Turing Test computer and person arrangement. The contest was launched in 1990 by Hugh Loebner and there are bronze, silver, and gold coin prizes, plus money. •So far, there have only been winners of the bronze medal and a $4,000 award. 32 Silver – a one-time-only prize plus $25,000 offered for the first program that judges cannot distinguish from a real human. Gold plus $100,000 for the first program that judges cannot distinguish from a real human in a Turing test that includes deciphering and understanding text, visual, and auditory input. Once this is achieved, the annual competition will end. . • Rule-based or Expert systems - Knowledge bases consisting of hundreds or thousands of rules of the form: • IF (condition) THEN (action). • Use rules to store knowledge (“rule-based”). • The rules are usually gathered from experts in the field being represented (“expert system”). • Most widely used knowledge model in the commercial world. IF (it is raining AND you must go outside) THEN (put on your raincoat) • Rules can fire off a chain of other rules IF (raincoat is on) THEN (you will not get wet) Expert Systems 36 Gardener Expert System Example Expert Systems Named abbreviations that represent conclusions: • NONE—apply no treatment at this time • TURF—apply a turf-building treatment • WEED—apply a weed-killing treatment • BUG—apply a bug-killing treatment • FEED—apply a basic fertilizer treatment • WEED & FEED—apply a weed-killing and fertilizer combination treatment 37 Expert Systems Some rules • if (THE CURRENT DAY – LAST DAY IS LESS THAN 30) then NONE • if (SEASON = winter) then not BUGS • if (BARE) then TURF • if (SPARSE and not WEEDS) then FEED • if (BUGS and not SPARSE) then BUG • if (WEEDS and not SPARSE) then WEED • if (WEEDS and SPARSE) then WEED & FEED 40 Expert Systems An execution of our inference engine • System: Does the lawn have large, bare areas? • User: No • System: Does the lawn show evidence of bugs? • User: No • System: Is the lawn generally thin? • User: Yes • System: Does the lawn contain significant weeds? • User: Yes • System: You should apply a weed-killing and fertilizer combination treatment. 41 42 2) Semantic (word description) Networks Semantic network A knowledge representation technique that focuses on the relationships between objects A directed graph or word chart is used to represent a semantic network or net 45 An example is a Search tree for playing the game Tic-Tac-Toe, as shown below. This image depicts many of the possible paths that the game can take from the having the first 2 rows filled, as shown: THE HUMAN BRAIN AND NEURONS IN IT A REVIEW BEFORE THE DISCUSSION ABOUT 4) NEURAL NETS 46 THE BRAIN IS DIVIDED INTO 4 LOBES AND THE CEREBELLUM WHICH IS LOCATED AT THE BOTTOM, BACK AREA 47 50 There are around 86 billion neurons in the brain. To reach this huge target, a developing fetus must create around 250,000 neurons per minute ! Each neuron is connected to at least 10,000 others – giving well over 1,000 trillion connections (1 quadrillion connections). They all connect at a junction called a synapse, which can be electrical or a higher percentage of them are chemical. 51 Incoming signals to the neuron can be either excitatory – which means they tend to make the neuron fire (generate an electrical impulse) – or inhibitory – which means that they tend to keep the neuron from firing. A single neuron may have more than one set of dendrites, and may receive many thousands of input signals. Whether or not a neuron is excited into firing an impulse depends on the sum of all of the excitatory and inhibitory signals it receives. If the neuron does end up firing, the nerve impulse is conducted down the axon. 52 How synapses work - Neurons are connected to each other at a location called a Synapse, so that they can communicate messages Amazingly, where each cell connects with the other one, NONE of these cells ever touch each other !! The signal that is carried from the first nerve fiber to the next one is transmitted by an electrical signal or a chemical one, up to a speed of 268 miles per hour ! There is new evidence that both types closely interact with each other and that the transmission of a nerve signal is both chemical and electrical, which is actually required for normal brain development and function. https://www.youtu be.com/watch?v=m ItV4rC57kM&t=10s 55 An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. 12-56 ARTIFICIAL NEURAL NETWORK • Artificial neurons: Commonly called processing elements, are modeled after real neurons of humans and other animals. • Has many inputs and one output. • The inputs are signals that are strengthened or weakened (weighted). • If the sum of all the signals is strong enough, the neuron will put out a signal to the next neuron output of a 1. Output Artificial NeuronInputs 57 Artificial Neural Networks Training The process of adjusting the weights and threshold values in a neural net How does this all work? Train a neural net to recognize An eagle in a picture. Given one output value per pixel, train network to produce an output value of 1 for every pixel that contributes to the eagle and 0 for every one that doesn’t. Why “meaning” is the central concept of AI • For an agent to be “intelligent”, it must be able to understand the meaning of information. • Information is acquired / delivered / conveyed in messages which are phrased in a selected representation language. • There are two sides in information exchange: the source (text, image, person, program, etc.) and the receiver (person or an AI agent). They must speak the same “language” for the information to be exchanged in a meaningful way. • The receiver must have the ability to interpret the information correctly according to the intended by the source meaning or semantics of it. MEANING = SEMANTICS 60 i: de al Machine Learning > Remeieatacuy A subset of Al that includes abstruse statistical techniques that enable machines to improve at tasks with experience. The nts of dat category includes deep learning 61 62 Machine Learning The phrase ‘machine learning’ dates back to the middle of the last century where Arthur Samuel in 1959 defined machine learning as “the ability to learn without being explicitly programmed.” Machine learning is a type of AI that helps a computer’s ability to learn and essentially teach itself to evolve as it becomes exposed to new and ever-changing data. For example, Facebook’s news feed uses machine learning in an effort to personalize each individual’s feed based on what they like. 65 For example, a deep learning algorithm could be trained to ‘learn’ how a dog looks like. It would take an enormous dataset of images for it to understand the minor details that distinguish a dog from a wolf or a fox. INDUSTRY ADOPTION High Low TRANSITORY NECESSARY Open source frameworks Facial e recognition Conversational Predictive Edge agents maintenance “Sr Cyber threat Medical hunting ® imaging & E-commerce siaponstos Synthetic ‘search e e training data @ Drug discovery e a e@ Crop. Back office itor automation Language ™onvtoring translation Anti-counterfeit @ Check-cut ree retail @ Advanioedl Kanal S Reinforcement er yanerkes jeare Auto claims learning Clinical trial & Seen e a enrollment Network Next-gen GANs e optimization es @ Federated e Capsule Networks leaning EXPERIMENTAL THREATENING Low MARKET STRENGTH High Application: Computer vision Application: Natural language processing/synthesis Application: Predictive intelligence Architecture Infrastructure 66 67 CONCERNS ABOUT AI TAKING OVER THE WORLD The computer that wins at games of Chess or Go, is analyzing data for patterns. It has no idea it’s playing Go as opposed to golf, or what would happen if more than half of a Go board was pushed beyond the edge of a table. When you ask Amazon’s Alexa to reserve you a table at a restaurant you name, its voice recognition system, made very accurate by machine learning, saves you the time of entering a request in Open Table’s reservation system. But Alexa doesn’t know what a restaurant is or what eating is. If you asked it to book you a table for two at 6 p.m. at the Mayo Clinic, it would try. Oem Facebook.com/BizarroComied | Diet Y Kind atures [tlt ©2018 BIZARKO STupi0g Thanke for the weather forecact, Alexa. TLE SS = ARTIFICIAL INDIFFERENCE. | 70  YOURSELVES THE END