Recomendados
Más contenido relacionado
La actualidad más candente
La actualidad más candente (20)
Similar a AI for Everyone: Master the Basics
Similar a AI for Everyone: Master the Basics (20)
Último
Último (20)
AI for Everyone: Master the Basics
- 1. AI for Everyone: Master the Basics IBM: AI0101EN
- 2. CONTENT • Module 1- What is AI? Applications and examples of AI • Module 2- AI Concepts, Terminology, and Application • Module 3- AI: Issues, Concerns and Ethical Considerations • Module 4- The Future with AI, and AI in Action
- 3. Module 1 What is AI ? Understanding its applications and use cases and how it is transforming our lives.
- 4. What is AI? • Anything that make machines act smarter • Augmented intelligence • A multidisciplinary field A set of mathematical algorithms that enable us to have computers find out very deep patterns that we may not have even known exist, without us having to hard code them manually.
- 5. What is AI? Based on strength, breadth and application, AI can be described in different ways- • Weak or Narrow AI (example- Language translators, virtual assistants, self driving cars) • Strong AI or Generalized AI • Super AI or Conscious AI (We have currently achieved narrow AI only)
- 6. How does AI learn? - Supervised Learning - Unsupervised Learning - Reinforcement Learning Machines are provided with ability to examine and create learning models
- 7. Applications of AI • Healthcare • Education • Finance • Customer Service • Oil and Gas Company
- 8. Applications of AI • Computer vision • Robotics and Automation • Collaborative Robots (Cobots) • Autonomous Vehicles
- 9. IBM Watson • Jeopardy! • The 60th Annual Grammy Awards • Fantasy Sports
- 10. Impact of AI AI is impacting the quality of our daily lives- • Netflix queue • Navigation App • Search Engine • Keeping spams out of our inboxes • Reminding us of important events • Monitoring our investments • Preventing financial crimes
- 11. Impact of AI According to a study by PWC, $16 trillion of GDP will be added between now and 2030 on the basis of AI. It will impact not just the IT industry but virtually every industry and aspect of our lives.
- 12. Business cases for the next 10,000 startups are easy to predict. I have ‘X’ and I will add AI to my ‘X. Everything we do, everything we touch is going to be enhanced by AI. We get great benefits if we take any device, any machine, and we make it a little smarter. The benefit of this is exponential. - Kelvin Kelly, Editor for Wired Magazine
- 13. Module 2 AI Concepts, Terminology and Applications • Define basic AI concepts • Explain Machine Learning, Deep Learning and Neural Networks • Explain the application areas of AI
- 14. Cognitive Computing • Involves perception, learning and reasoning • Elements are similar to human knowledge : (a)Observe (b)Interpret (c)Evaluate (d) Decide • Understand unstructured data (80% of current data ) • Rely on Natural Language • Understand context • Learn by interacting with us
- 15. Artificial Intelligence (AI) Branch of computer science dealing with the simulation of intelligent behaviour. AI Systems Planning Creativity Social Intelligence Manipulation Knowledge Problem Solving Reasoning Learning
- 16. Machine Learning (ML) A subset of AI that uses computer algorithms to analyze data and make intelligent decisions based on what it has learned, without being explicitly programmed. Trained with large data sets Learn from examples Do not follow rule based algorithms
- 17. Machine Learning (ML) Traditional Programming • Use statistical analysis to create an algorithm • Submit data and rules to get answers • Based on algorithm • Algorithm remains constant Machine Learning • Take data and responses to create an algorithm • Submit data and answers and get the rules • Based on finding common patterns • Can be continuously trained
- 18. Machine Learning (ML) Supervised Learning Algorithm on human labeled data is trained Unsupervised Learning Algorithm is provided unlabeled data, it extract deduction and find patterns by itself Reinforcement Learning Algorithm is provided a set of rules and restrictions and it learns how to achieve its goals
- 19. Machine Learning (ML) Supervised Learning Regression Classification Neural Networks
- 20. Machine Learning (ML) We take the data set and split it into : • Data used to train the algorithmTraining • Data used to validate our results and fine tune the algorithm parametersValidation • Data that the model has never seen before and they are used to evaluate how good is our model (accuracy, precision, recall) Testing
- 21. Deep Learning (DL) • A specialized subset of ML that uses layered neural network to simulate human decision making. DL Algorithm • Categorize information • Identify patterns • Enables AI to learn continuously • Improves quality of results
- 22. Deep Learning (DL) • Allow systems to learn from unstructured data • Comprehend context and intention • depends on various levels of process units • DL algorithm improves with data • Used in image captioning, autonomous cars Artificial Intelligence Machine Learning Deep Learning
- 23. Artificial Neural Network (ANN) Collection of small computing units called neurons that take incoming data and learn to make decisions over time. Takes inspiration from biological neural network Often many layers deep Makes DL algorithm more efficient with increased data volume
- 24. Artificial Neural Network (ANN) ANN learn though a process called BACK PROPOGATION adjustments are made to reduce errors an error function determines how far there is the given output of the desired output. Inputs are plugged into the network, and outputs are determined
- 25. Artificial Neural Network (ANN) • A collection of neurons is called a LAYER. • A neural network has : a) One input layer b) One output layer c) One or more hidden layers A neural network with more than one hidden layer is called a DEEP NEURAL NETWORK.
- 26. Artificial Neural Network (ANN) PERCEPTRONS- • single-layer neural networks • input nodes directly connected to an output node • Hidden and exit nodes have a property called bias (bias) • activation function determines how a node responds to its inputs.
- 27. Artificial Neural Network (ANN)
- 28. Artificial Neural Network (ANN) CONVOLUTION NEURAL NETWORK (CNN)- • Multilayer neural networks • Detect simple structures and put together to build more complex features • Each layer performs a convolution at the output of the previous layer • useful in image processing, video recognition and natural language processing.
- 29. Artificial Neural Network (ANN) Convolution Neural Network
- 30. Artificial Neural Network (ANN) RECURRENT NEURAL NETWORK (RNN)- • Output from the output layer are fed back to the set of input units • Depend on previous observation to produce output • Store information about time • Suitable for forecasting applications
- 31. Artificial Neural Network (ANN)
- 32. Artificial Neural Network (ANN) GENERAL NEURAL NETWORK (GNN)- • Entry is processed through a number of layers • Output assumes that the two successive inputs are independent of each other Exception- when we need to consider the context in which a word has been pronounced, such situations must take into account the dependence on the above observations to get the result.
- 33. Artificial Neural Network (ANN) General Neural Network
- 34. Artificial Neural Network (ANN) GENERATIVE ADVERSARIAL NETWORK (GAN)- • Uses two neural networks, pitting one against the other (“adversarial”) • Used to generate new, synthetic instances of data that can pass for real data. • Example- image generation, video generation and voice generation
- 35. Artificial Neural Network (ANN) Generative Adversarial Network
- 36. Data Science • Covers entire data processing methodology • Uses AI techniques to extracts results from large volumes of data • Intersection between AI and Data Science but one is not the subset of other • Both involve use of big data • Includes mathematics, statistics, data visualization, ML, and much more
- 37. AI Application Areas • Natural Language Processing (most complex data to work in ML) • Computer Vision (visual data comprehension) • Audio based data (converting text-to-speech and speech-to-text)
- 38. Module 3 Issues and concerns surrounding AI, including - ethical considerations, bias, jobs, etc. - their impact on society. (This information will help in having an informed discussion on the costs and benefits of AI, and reassure decision makers about implementing an AI solution)
- 39. Issues /Concerns Surrounding AI • Privacy • Fear of job • Ethics • Uncertainty
- 40. AI and Ethical Concerns • Capability to track humans endlessly. What is allowed and what is not. • Misinterpreted • Human problem • AI- tram problem (autonomous vehicles) • Transparency
- 41. AI Ethics Some conversation about Ethics assume that AI will become sentient • Self aware • Develop its own moral code • Turn against its inventor
- 42. AI Ethics “that in the very long term, the super intelligence of the machines could eventually equal human intelligence, and that when that happens, there may be risks to human society.” - Nick Bostrom
- 43. AI Ethics "The rise of a powerful AI will be the best or the worst thing to happen to humanity. We do not yet know which.” - Stephan Hawkings "AI is more dangerous than nuclear weapons” - Elon Musk
- 44. AI Bias It exist because of two reasons: • Lack of representative data • Intrinsically biased
- 45. AI Bias AI developers can guide against reducing bias by providing: • Effective training data • Regular Tests and audit
- 46. AI Trust Trust is the key in developing useful, successful AI System • Transparency • Accountability • Privacy • Lack of Bias
- 47. Jobs and AI • Automatable and manual task will be replaced • New job opportunities • Responsibility of corporations to re-skill the people • Workers should keep learning and look for new qualifications
- 48. Employment and AI Repetitive and rule based job will be replaced – Bank tellers – Salesperson – Call center operators – Drivers WEF Report : AI will displace 75 million jobs but will foster 133 million new jobs in the coming year
- 49. Principles for Ethical AI To ensure that AI and cognitive systems are ethical, trustworthy and socially responsible, IBM follows 3 principles: • Purpose • Transparency • Skills
- 50. Module 4 The Future with AI, and AI in action (learning about the current thinking on the future with AI, as well as advice from experts about how to learn and start a career in AI)
- 51. Evolution and Future of AI • Early AI researchers were interested in games like CHESS. • Watson • Understanding Unstructured data • Symbiotic Interaction
- 52. Future with AI With AI technology is : • Going to be faster • It will improve • It will be cheaper • It’s going to happen very quickly (example : DeepMind’s Go game) AI and robotic technology will improve the quality of life across the spectrum of society
- 53. The AI Ladder After modernizing all the data on a single platform that runs on any cloud, the AI Ladder has 4 steps: 1. Collect 2. Organize 3. Analyze 4. Infuse
- 54. Career in AI • Get skills whenever you can. • demonstrate your abilities (projects, exercises, blog posts, articles) • teach at conferences to demonstrate your skills in public • connect with local experts to have a better idea of your professional roadmap
- 55. Learn AI • Be passionate about technology • Learn technological concepts • Learn programming languages like Python, Julia, SWIFT • Learn real mathematics behind neural networks • Try to implement neural network from scratch • Use different libraries and toolkits that allow to create applications in ML
- 56. Hotbeds of AI Innovation • San Francisco – Bay Area (AI and robotic startups) • Boston • Seattle • Canada – Toronto, Montreal • Europe • Asia – China, India
- 57. AI in Action - Activity • Create a free account on IBM cloud • Create a Watson Studio Resource Scenario • Build a model using IBM Watson Studio. • Create a Project • Explore the Examples
- 59. THANK YOU !
Notas del editor
- Augmented Intelligence: AI does not try to replace human intelligence but expand human capabilities. Internet has given us much unstructured data, augmented intelligence makes the information available to the subject matter experts to help make evidence based informed decisions. A multidisciplinary field- Computer science and electrical engineering determine how AI is implemented in software and hardware. Mathematics and statistics determined viable models and measure performance. Because AI is modeled on how we believe the brain works, psychology and linguistics play an essential role in understanding how AI might work and philosophy provides guidance on intelligence and ethical considerations.
- Weak or Narrow AI - AI that is applied to a specific domain. For example, language translators, virtual assistants, self-driving car, AI powered web searches, recommendation engines, and intelligent spam filters. Applied AI can perform specific tasks, but not learn new ones, making decisions based on programmed algorithms, and training data. Strong AI or Generalizes AI - AI that can interact and operate a wide variety of independent and unrelated tasks. It can learn new tasks to solve new problems, and it does this by teaching itself new strategies. Generalized intelligence is the combination of many AI strategies that learn from experience and can perform at a human level of intelligence. Super AI or conscious AI- AI with human level consciousness, which would require it to be self-aware. Because we are not yet able to adequately define what consciousness is, it's unlikely that we will be able to create a conscious AI in the near future.
- Healthcare- AI is used to question patients and run basic diagnoses like real doctors. It's helping patients with Lou Gehrig's disease, for example, to regain their real voice in place of using a computerized voice. It is helping doctors to arrive at more accurate preliminary diagnoses, reading medical imaging, finding appropriate clinical trials for patients. It is not just influencing patient outcomes but also making operational processes less expensive. Education- AI is providing students with easy to learn conversational interfaces and on-demand online tutors. Customer Service- chatbots are improving customer experience by resolving queries on the spot and freeing up agents time for conversations that add value. Oil and Gas Company- Abu Dhabi National Oil Company faced problem to identify the best place for them to drill oil. Expert Grologists need to be trained in classification of rock samples to find the best place for oil (difficult, time consuming, costly) So, we use computer vision to classify these rock samples to identify the best location for drilling oil.
- Computer Vision- AI’s advances in computer vision helps to detect and label objects. -Helps autonomous cars to steer their way on streets and highways and avoid hitting obstacles. -The algorithms detect facial features and images and compare them with databases of face profiles. This is what allows consumer devices to authenticate the identities of their owners through facial recognition, social media apps to detect and tag users, and law enforcement agencies to identify criminals in video feeds - It helps to automate tasks such as detecting cancerous moles in skin images or finding symptoms in x-ray and MRI scan. -Airport Security, where AI is making it possible for X-ray scanners to flag images that may look suspicious. Robotics and Automation - AI is making it possible for robots to perceive unpredictable environments around them in order to decide on the next steps. Collaborative Robots (Cobots)- Robots that are designed to work in and around and with people. And that presents many challenges, because we want robots act intelligently and interact with humans in a natural way. And that requires understanding how people behave, which requires intelligence. (Manufacturing applications, warehousing logistics, to lift heavy container, move items on the stocking, shelf stocking purposes)
- Jeopardy! – In 2011, the Watson computer system competed on Jeopardy! Against champions Brad Rutter and Ken Jennings, winning the first place prize of $ 1 million. The 60th Annual Grammy Awards (powered by IBM Watson)- Watson teamed up with the Academy to deliver an amplified Grammy experience for millions of fans. Watson helped Grammy’s with their workflows for their digital production. It took five hours of red carpet coverage with 5,000 artists, making that trip down the carpet with a 100,000 photos that were shot. Watson used AI analyze colours, patterns, and silhouettes of every single outfit that has passed through. So it’s able to see all the dominant styles and compare them to Grammy shows in the past. It also analyzed the emotions of Grammy nominated song lyrics over the last 60 years. And actually identified the emotional themes in music and categorized them as joy, sadness, and everything else in between. It is taking the guesswork out of the unstructured data. Fantasy Sports- Watson collaborated with ESPN to serve 10 million users of the ESPN Fantasy App sharing insights that help them make better decisions to win their weekly matchups. ESPN teamed up with IBM to add a powerful feature to their fantasy football platform. Fantasy football generates a huge volume of content - articles, blogs, videos, podcasts (unstructured data ). Watson was built to analyze that kind of information and turn it into usable insights. Watson was able to develop a scoring range for thousands of players, upsides and their downside. It estimated the chances of player will exceed their upside or fall below the downside. Watson even assesses a player's media buzz and their likelihood to play.
- Regression - Regression models are built looking at the relationships between the x characteristics and the result y, where y is a continuous variable. Basically, the Regression estimates continuous values. Classification- It focuses on discreet values that it identifies. It is the process of predicting the class of a given data point. Classifiers use some training data to understand how some input variables are related to that class. Neural Networks- Neural Networks refer to structures that mimic the structure of the human brain. It uses a reward function to penalize bad actions and reward good actions.
- Back Propogation - uses training dataset that match known entries to desired result
- Hidden Layers- They simulate type of activities that develop in the human brain. They receive weighted inputs and produce an output through an activation function.
- Perceptrons are the simplest types and old neural networks. They are single-layer neural networks consisting of input nodes directly connected to an output node. The input layers forward the input values to the next layer, multiplying by a factor and adding the results. Hidden layers receive input from other nodes and send their output to other nodes. Hidden and exit nodes have a property called bias (bias), which is a special type of factor that is applied to a node after the other input variables are considered. Finally, an activation function determines how a node responds to its inputs. The function runs against the sum of the inputs and the bias, and then the result is sent as an output. Activation functions can take different forms, and choosing them is a critical component for the success of a neural network.
- A convolution is a mathematical operation, where one function is applied to another function and the result is a mixture of the two functions.
- They perform the same task for each element of a sequence, with previous outputs that feed the inputs of the subsequent stage
- Natural Language Processing – It is a subset of AI that allows computers to understand the meaning of human language. Natural language processing uses machine learning and deep learning algorithms for discern the semantic meaning of a word. It does this by deconstructing grammar sentences, relationally and structurally and understanding the context of use. Computer Vision – It allow computers to identify and process objects into images and videos. It allows the digital world to interact with the physical world. The field of computer vision has made great strides in recent years and outperform humans in related tasks with object detection and tagging, due to advances in deep learning and neural networks. This technology allows autonomous cars understand what is around you. It plays a vital role in facial recognition applications that allow computers to match the images of people's faces with their identities. It also plays a crucial role in augmented and mixed reality. It is the technology that allows computing devices like smartphones, tablets and smart glasses to overlay and embed virtual objects in real world images. Online photo libraries, such as Google Photos, use machine vision to detect objects and classify images according to the type of content they contain. Audio based data- Instead of coding, system is provided with voice samples and corresponding text. Used to enhance customer experience.
- Privacy – It’s challenging to convince people that their information is secure. We have to prove that data is being anonymously used. We have to educate the people to overcome the barrier. Fear of job – New technology gives everyone a bit of anguish. Change is terrifying. It spreads through social media. It is natural. AI will replace biological intelligence and humans will become irrelevant. Ethics- It is one of the gray areas. We still have to think about the rules behind ML. example- Ownership of the music generated by neural networks? It’s a difficult question but the answer is simple at its core. Since ML is just another algorithm, so whoever owns the algorithm or the right to use the algorithm is the owner of the piece of music it generated. Uncertainty- Humans have a history of creating technology that is destructive (nuclear weapon exploded in New Mexico Desert on July 16, 1945; deforestation; seas dried up). We are looking for plan B of relocating civilization to Mars. AI is no different. It can be used for harmful purposes or can be our greatest invention, ever. It’s how we apply it.
- Misinterpreted- there have been technologies that have been used with bad ends and with good purposes. No matter how much good intentions we put them to use, it can always be misinterpreted in some way to be used for evil. Like fire. It is excellent for cooking, but also to burn people's houses. But at the same time, you can use it to drive away the people who were burning people's houses. So that's a good analogy of what we are doing with the machine learning. Humans will use it for bad things, but we will use machine learning to counter it. But it won't become something so horrible for machines or for human. Human Problem- . It can be used to reduce crime or to simplify the work of forces, security or can be used for dire reasons like for a dictatorial government, enforce your will on the people, to stop and suppress dissent, to suppress democracy, etc. So, ethics is not a technological problem, ethics is a human problem. So companies like Apple, IBM, Amazon, Microsoft, DeepMind, Google, all have joined and are advocating the best ways to apply ethics to this technology. AI – tram problem (autonomous vehicles)- If the vehicle has to decide what accident to cause. If you have to choose between hitting a signal injuring passengers in the vehicle, or collide with pedestrians on the side of the road, but potentially save passengers. How do we make those decisions? it's not clear how we can adequately define regulations so that different companies that these vehicles are producing behave in an ethical and consistent way that meets our expectations. And who would be to blame for the choice the vehicle makes. We have faced these issues throughout history. For example, if you take a look about 100 years ago when humans drove horse carriages. Suppose there was a traveling horse carriage. Had a pedestrian in the middle, a strange noise, or did something you didn't like the horse, and the horse stomped on the pedestrian. Who do you blame? You can't blame the carriage driver, because he had no control of the situation. You cannot punish the horse. What's the point of that? Transparency-A person is entitled to know when they are speaking to a human being and when they are speaking to a bot. Since bots are indistinguishable from humans, especially during short conversation, there is a lack of trust in AI system, exacerbated by lack of transparency.
- Lack of representative data - Microsoft Surface Facial Recognition had a hard time in understanding and recognizing the faces of people in certain demographic groups because there was not enough representative data on the faces of those people in the training data set that Microsoft used. Intrinsically biased- ML is based on a fundamental assumption of bias. ML skew certain data points of input to assign to other output data points. This is how they fundamentally work. We can increase data or allow less biased data to reduce bias consciously. But subconsciously, we end up applying certain type of biases that we can’t control. A combined approach is needed. Numerous researchers are working to solve this problem. It’s an area of active research.
- Experts must guide against reducing bias (gender, racial, social). Example –Image recognition software (unconscious bias of its developers due to lack of training data). - It identified scenes showing kitchen, laundry with women and sports and shooting with men. - More effective in identifying individuals with lighter skin tone than individuals with darker skin tone (because training data provided to the AI was not sufficiently varied). ** Very risky in real life situations like if used in courts to help predict probability of a person re offending.
- Transparency - people must be aware when they interact with an AI system and understand what their expectation for the interaction should be. Accountability- developers must create AI systems with algorithmic traceability, so that any unexpected results can be tracked and undone if necessary. Privacy- Personal information should always be protected. Lack of Bias- developers should use representative training data to avoid bias and conduct regular audits to detect any bias creeping in.
- We would not regret having invented the fridge, the bicycle or the car. Neither you nor I would probably think of creating a time machine to go back and prevent it from being invented. Because it has transformed our lives in ways we could never have imagined, in the same sense in which AI could potentially transform our lives. The ice pickers tried to get big blocks of ice cubes and take them to the cities, to homes so they can refrigerate food, those jobs are replaced by the fridge, and also horse-drawn carriages, were replaced by bicycles and also by automobiles. So technology is always going to transform society. Anything that is repeatable even in the office environment, repeatable tasks, the generation of documents in the industrial environment, repeatable manufacturing, repeatable assembly, these are the jobs that are going to evaporate, but they will be replaced by a huge set of new opportunities, where we need people who understand how these technologies work, and you can maximize its efficiency in its applications. Will robots take control? Are they going to remove the jobs to large numbers of people? NO. In fact, robots are very likely to create new jobs. So in the areas of robot maintenance, manufacturing, design, all new positions that I think will be available.
- Call center Operators: The AI assistant bots answer calls in call centers, answer questions on the websites, perform tasks on cell phones and many other things. The bots can be used to perform tasks that are simple for many workers, releasing those people to take care of more complex tasks or replacing them entirely, depending on the implementation. Bots can be integrated into an organization's existing systems, providing help and support to people and facilitating their work. Drivers: Autonomous driving has become a reality, the jobs of many millions of drivers around the world are in danger, some of whom have found new sources of income in recent years driving for companies like Uber, Lyft, Ola and DD.
- Purpose: Cognitive systems will not become conscious, or gain independent agency, but will always remain under the control of humans. Cognitive systems will be embedded in systems used to enhance human capabilities. Cognitive systems must be built with people in the industry. Transparency: Transparency is required to gain public trust and confidence in AI judgments and decisions, so that cognitive systems can be used to their full potential. Skills: IBM will work to help people gain the knowledge needed to engage with AI systems safely and securely. IBM will also help people develop the skills necessary to perform the new kinds of jobs that AI creates.
- Games- Researchers were interested because they were extremely complex as they had large number of possible positions and available achievements, but they are simple in a way that the movements are well defined, the objectives are well defined. Watson- It saw the development of cognitive computing. Questions that IBM was able to answer in Jeopardy were not by searching on the database rather it required information retrieval through many different information resources. Then the combination of these with the use of machine learning Watson could come up with answers. Understanding unstructured data- All industries, from oil and gas to health, through the media, entertainment and retail, are being inundated by a tsunami of unstructured data. It can be multimedia, it can be images, it can be video, it can be text. The ability to understand the data is becoming critical. Symbiotic Interaction- There is an intersection between what the computer can do and what people are capable of. What is going to be really interesting is see what's the best way that have a really symbiotic interaction, building on the efforts of others to collectively solve a problem.
- DeepMind’s Go game - . It is a 2,500 year old game, which you won against a human opponent. But what is more surprising is that while the first system was able to exceed 2,500 years of human history in the game, the second generation of that system was capable of outperforming the first generation of the system in less than a year. And it only took him about 40 hours of training to to be able to reach that level of skill in that game. And he won 100 games out of 100
- AI can predict and inform future outcomes. It enables people to do higher value work and businesses to imagine new models. It can automate decisions, processes, experiences but AI is not magic. The truth is there is no AI without IA or information architecture, but many organizations can't start because 80% of their data is locked in silos and not business-ready. So, how do you turn your aspirations into outcomes? Through AI Success Ladder: Collect - collect the data to make it simple and accessible Really think about the models you need to train Organize- organize your data to create a business-ready analytics foundation for those AI models Analyze- analyze your data both for trust and transparency Because there's no use in applying and scaling AI if you can't explain the outcome, detect bias or prove its accuracy Infuse- realize its full value Inside of the apps and processes that control your everyday work. Infuse or you begin to operationalise AI throughout the business
- IBM cloud link: https://cloud.ibm.com/login