AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
The document discusses using generative AI to improve learning products by making them better, stronger, and faster. It provides examples of using generative models for game creation, runtime design, and postmortem data analysis. It also addresses ethics and copyright challenges and considers generative AI as both a tool and potential friend. The document explores what models are, how they work, examples of applications, and resources for staying up to date on generative AI advances.
How Does Generative AI Actually Work? (a quick semi-technical introduction to...ssuser4edc93
This document provides a technical introduction to large language models (LLMs). It explains that LLMs are based on simple probabilities derived from their massive training corpora, containing trillions of examples. The document then discusses several key aspects of how LLMs work, including that they function as a form of "lossy text compression" by encoding patterns and relationships in their training data. It also outlines some of the key elements in the architecture and training of the most advanced LLMs, such as GPT-4, focusing on their huge scale, transformer architecture, and use of reinforcement learning from human feedback.
Introduction to artifcial intelligence
Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as AGI (Artificial General Intelligence) while attempts to emulate 'natural' intelligence have been called ABI (Artificial Biological Intelligence). Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving"
A public talk "AI and the Professions of the Future", held on 29 April 2023 in Veliko Tarnovo by Svetlin Nakov. Main topics:
AI is here today --> take attention to it!
- ChatGPT: revolution in language AI
- Playground AI – AI for image generation
AI and the future professions
- AI-replaceable professions
- AI-resistant professions
AI in Education
Ethics in AI
What is Artificial Intelligence | Artificial Intelligence Tutorial For Beginn...Edureka!
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This tutorial on Artificial Intelligence gives you a brief introduction to AI discussing how it can be a threat as well as useful. This tutorial covers the following topics:
1. AI as a threat
2. What is AI?
3. History of AI
4. Machine Learning & Deep Learning examples
5. Dependency on AI
6.Applications of AI
7. AI Course at Edureka - https://goo.gl/VWNeAu
For more information, please write back to us at sales@edureka.co
Call us at IN: 9606058406 / US: 18338555775
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
The document provides an overview of deep learning, including its history, key concepts, applications, and recent advances. It discusses the evolution of deep learning techniques like convolutional neural networks, recurrent neural networks, generative adversarial networks, and their applications in computer vision, natural language processing, and games. Examples include deep learning for image recognition, generation, segmentation, captioning, and more.
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
The document provides an overview of generative AI, including its key concepts and applications. It discusses transformer models versus neural networks, explaining that transformer models use self-attention to capture long-range dependencies in sequential data like text. Large language models (LLMs) based on the transformer architecture have shown strong performance in natural language generation tasks. The document outlines the evolution of generative AI techniques from early machine learning to modern large pretrained models. It also surveys some commercial generative AI applications in industries like healthcare, finance, and gaming.
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
The document discusses using generative AI to improve learning products by making them better, stronger, and faster. It provides examples of using generative models for game creation, runtime design, and postmortem data analysis. It also addresses ethics and copyright challenges and considers generative AI as both a tool and potential friend. The document explores what models are, how they work, examples of applications, and resources for staying up to date on generative AI advances.
How Does Generative AI Actually Work? (a quick semi-technical introduction to...ssuser4edc93
This document provides a technical introduction to large language models (LLMs). It explains that LLMs are based on simple probabilities derived from their massive training corpora, containing trillions of examples. The document then discusses several key aspects of how LLMs work, including that they function as a form of "lossy text compression" by encoding patterns and relationships in their training data. It also outlines some of the key elements in the architecture and training of the most advanced LLMs, such as GPT-4, focusing on their huge scale, transformer architecture, and use of reinforcement learning from human feedback.
Introduction to artifcial intelligence
Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as AGI (Artificial General Intelligence) while attempts to emulate 'natural' intelligence have been called ABI (Artificial Biological Intelligence). Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving"
A public talk "AI and the Professions of the Future", held on 29 April 2023 in Veliko Tarnovo by Svetlin Nakov. Main topics:
AI is here today --> take attention to it!
- ChatGPT: revolution in language AI
- Playground AI – AI for image generation
AI and the future professions
- AI-replaceable professions
- AI-resistant professions
AI in Education
Ethics in AI
What is Artificial Intelligence | Artificial Intelligence Tutorial For Beginn...Edureka!
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This tutorial on Artificial Intelligence gives you a brief introduction to AI discussing how it can be a threat as well as useful. This tutorial covers the following topics:
1. AI as a threat
2. What is AI?
3. History of AI
4. Machine Learning & Deep Learning examples
5. Dependency on AI
6.Applications of AI
7. AI Course at Edureka - https://goo.gl/VWNeAu
For more information, please write back to us at sales@edureka.co
Call us at IN: 9606058406 / US: 18338555775
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
The document provides an overview of deep learning, including its history, key concepts, applications, and recent advances. It discusses the evolution of deep learning techniques like convolutional neural networks, recurrent neural networks, generative adversarial networks, and their applications in computer vision, natural language processing, and games. Examples include deep learning for image recognition, generation, segmentation, captioning, and more.
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
The document provides an overview of generative AI, including its key concepts and applications. It discusses transformer models versus neural networks, explaining that transformer models use self-attention to capture long-range dependencies in sequential data like text. Large language models (LLMs) based on the transformer architecture have shown strong performance in natural language generation tasks. The document outlines the evolution of generative AI techniques from early machine learning to modern large pretrained models. It also surveys some commercial generative AI applications in industries like healthcare, finance, and gaming.
The document discusses artificial intelligence and provides an overview of key topics including:
- A brief history of AI beginning with the 1956 Dartmouth conference where the field was first proposed.
- Types of AI such as artificial weak intelligence, artificial hybrid intelligence, and artificial strong intelligence.
- Applications of AI such as computer vision, machine translation, and robotics.
- Progress in deep learning including speech recognition, computer vision, and machine translation.
- Demos of AI services including a cognitive race between AWS and Azure and using an AWS bot with Lex.
This talk overviews my background as a female data scientist, introduces many types of generative AI, discusses potential use cases, highlights the need for representation in generative AI, and showcases a few tools that currently exist.
Presenting the landscape of AI/ML in 2023 by introducing a quick summary of the last 10 years of its progress, current situation, and looking at things happening behind the scene.
Game changing AI Startups need great AI system. Learn the basic concepts and importance of AI to change the way you build a Startup. Its time to identify and develop skill sets to make better decisions.
Accelerate #AI workloads with Tesla V100 on #E2ECloud : http://bit.ly/E2EGPU
The document discusses generative AI and how it has evolved from earlier forms of AI like artificial intelligence, machine learning, and deep learning. It explains key concepts like generative adversarial networks, large language models, transformers, and techniques like reinforcement learning from human feedback and prompt engineering that are used to develop generative AI models. It also provides examples of using generative AI for image generation using diffusion models and how Stable Diffusion differs from earlier diffusion models by incorporating a text encoder and variational autoencoder.
The document discusses generative models and their applications in artificial intelligence. Generative adversarial networks (GANs) use two neural networks, a generator and discriminator, that compete against each other. The generator learns to generate new data that looks real by fooling the discriminator, while the discriminator learns to better identify real from fake data. GANs have been used for tasks like image generation and neural style transfer. They show potential to generate art, music and other creative forms through machine learning.
Top 10 Applications Of Artificial Intelligence | EdurekaEdureka!
YouTube Link: https://youtu.be/Y46zXHvUB1s
** Machine Learning Masters Program: https://www.edureka.co/masters-progra... **
This Edureka session on Applications Of Artificial Intelligence will help you understand how AI is impacting various domains such as banking, marketing, healthcare and so on.
Following are the topics covered in this PPT:
AI In Artificial Creativity
AI In Social Media
AI In Chatbots
AI In Autonomous Vehicles
AI In Space Exploration
AI In Gaming
AI In Banking & Finance
AI In Agriculture
AI In Healthcare
AI In Marketing
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This document discusses the ethical issues surrounding artificial intelligence. It begins by noting humanity's long-standing fascination with creating tools that can replace human labor. However, others have warned of the potential harms of AI if not developed with wisdom. The document then outlines some of the common fears associated with AI, such as technology becoming autonomous and reversing the master-servant role between humanity and our creations. It also examines themes from Frankenstein that continue to emerge in science fiction, such as the ambiguity of technology and whether it will ultimately benefit or hinder humanity. The document considers various impacts that highly advanced AI could have, such as economic and educational impacts, and concludes by emphasizing the importance of considering whether just because we can
Chat GPT 4 can pass the American state bar exam, but before you go expecting to see robot lawyers taking over the courtroom, hold your horses cowboys – we're not quite there yet. That being said, AI is becoming increasingly more human-like, and as a VC we need to start thinking about how this new wave of technology is going to affect the way we build and run businesses. What do we need to do differently? How can we make sure that our investment strategies are reflecting these changes? It's a brave new world out there, and we’ve got to keep the big picture in mind!
Sharing here with you what we at Cavalry Ventures found out during our Generative AI deep dive.
What Is The Artificial Intelligence Revolution And Why Does It Matter To Your...Bernard Marr
AI (Artificial Intelligence) is the most powerful technology humans have ever had access to. AI is going to revolutionize the world of business and society at large. In this article, we take a look at this AI revolution.
This presentation provides an overview of artificial intelligence (AI), including its definition, introduction, foundations, advantages, applications, and limitations. AI is defined as the intelligence demonstrated by machines and the branch of computer science which aims to create intelligent agents. The presentation traces the foundations of AI through various fields such as philosophy, mathematics, neuroscience, and computer engineering. It also outlines the advantages of AI, such as reducing errors and exploring new possibilities, and the potential disadvantages like overreliance on AI and job losses. The presentation concludes that while AI tools can help solve problems, they cannot replace human capabilities.
10 Wonderful Examples Of Using Artificial Intelligence (AI) For Good Bernard Marr
Artificial intelligence is being used in many positive ways to help address societal problems. Some examples discussed in the document include using AI in cancer screening and healthcare decision-making, saving bee populations by analyzing sensor data, creating apps to help people with disabilities, addressing climate change through climate modeling, aiding wildlife conservation efforts, combating world hunger through crop analysis, reducing inequality by correcting algorithmic bias, identifying "fake news", improving medical imaging analysis, and prioritizing infrastructure upgrades.
Conversational AI versus AI Data ScienceRazorthink
Uncover the difference between a conversational AI and AI data science in this informative presentation, as well as how AI is being used on the Enterprise level.
This presentation provides an introduction to artificial intelligence (AI), its applications, and risks. It defines AI as the ability of machines to perform tasks typically requiring human intelligence, such as understanding language, recognizing patterns, and making decisions. Applications of AI discussed include machine learning, improving efficiency in industries, and using AI in healthcare. Risks covered are potential for biased outcomes from training data, malicious use of AI, and advanced AI surpassing human intelligence. The presentation concludes it is important to consider both the benefits and risks/unintended consequences of AI development and deployment.
This document provides an overview of artificial intelligence (AI) including its history and key concepts. It discusses how philosophers like Hobbes and mathematicians like Boole laid the foundations for AI by exploring symbolic logic and operations. Landmark developments included Babbage's analytical machine, Turing's universal machine concept, and McCarthy coining the term "artificial intelligence". The document also outlines branches of AI like natural language processing, computer vision, robotics, problem solving, learning, and expert systems. It provides examples of applications and concludes by noting progress made in creating human-like artificial creatures remains limited.
Title: Incredible developments in Artificial intelligence which was the future scenario.
Here I discussed the with the major backbones of AI (Machine learning, Neural networks) types Machine learning and type of Artificial intelligence and with some real-time examples of AI and ML & Benefits and Future of AI with some pros and Cons of Artificial Intelligence.
This talk gives an introduction about Healthcare Use cases - The AI ladder and Lifestyle AI at Scale Themes The iterative nature of the workflow and some of the important components to be aware in developing AI health care solutions were being discussed. The different types of algorithms and when machine learning might be more appropriate in deep learning or the other way will also be discussed. Use cases in terms of examples are also shared as part of this presentation .
9 Examples of Artificial Intelligence in Use TodayIQVIS
Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans.
Industry analysts argue that artificial intelligence is the future – but if we look around, we are convinced that it’s not the future – it is the present. The given examples will explain the true meaning and context.
Read as a blog post here. http://www.iqvis.com/blog/9-powerful-examples-of-artificial-intelligence-in-use-today/
This whitepaper provides an overview of artificial intelligence (AI) and its commercialization. It discusses the history and development of AI from early pattern recognition (AI 1.0) to today's deep learning (AI 2.0) to the emerging contextual reasoning (AI 3.0). Key points include how transfer learning and increased computing power are driving new AI applications and how AI is being applied commercially in healthcare, manufacturing, logistics, and other industries. The document also addresses the global demand for AI talent and the challenges of developing reliable AI systems that can operate under changing conditions.
Artificial intelligence involves using machines to simulate human intelligence through techniques like machine learning and deep learning. The presentation traces the origins of AI back to 1956 with the goal of giving computers abilities like reasoning, understanding language, and perceiving the world. Opportunities for AI include autonomous delivery vehicles, personalized shopping experiences using computer vision, using robots for tedious tasks, and developing new drugs. The winners in AI will be those focusing on narrow domains with vast data and those able to achieve network effects by crowdsourcing or leveraging multiple data sources.
The document discusses artificial intelligence and provides an overview of key topics including:
- A brief history of AI beginning with the 1956 Dartmouth conference where the field was first proposed.
- Types of AI such as artificial weak intelligence, artificial hybrid intelligence, and artificial strong intelligence.
- Applications of AI such as computer vision, machine translation, and robotics.
- Progress in deep learning including speech recognition, computer vision, and machine translation.
- Demos of AI services including a cognitive race between AWS and Azure and using an AWS bot with Lex.
This talk overviews my background as a female data scientist, introduces many types of generative AI, discusses potential use cases, highlights the need for representation in generative AI, and showcases a few tools that currently exist.
Presenting the landscape of AI/ML in 2023 by introducing a quick summary of the last 10 years of its progress, current situation, and looking at things happening behind the scene.
Game changing AI Startups need great AI system. Learn the basic concepts and importance of AI to change the way you build a Startup. Its time to identify and develop skill sets to make better decisions.
Accelerate #AI workloads with Tesla V100 on #E2ECloud : http://bit.ly/E2EGPU
The document discusses generative AI and how it has evolved from earlier forms of AI like artificial intelligence, machine learning, and deep learning. It explains key concepts like generative adversarial networks, large language models, transformers, and techniques like reinforcement learning from human feedback and prompt engineering that are used to develop generative AI models. It also provides examples of using generative AI for image generation using diffusion models and how Stable Diffusion differs from earlier diffusion models by incorporating a text encoder and variational autoencoder.
The document discusses generative models and their applications in artificial intelligence. Generative adversarial networks (GANs) use two neural networks, a generator and discriminator, that compete against each other. The generator learns to generate new data that looks real by fooling the discriminator, while the discriminator learns to better identify real from fake data. GANs have been used for tasks like image generation and neural style transfer. They show potential to generate art, music and other creative forms through machine learning.
Top 10 Applications Of Artificial Intelligence | EdurekaEdureka!
YouTube Link: https://youtu.be/Y46zXHvUB1s
** Machine Learning Masters Program: https://www.edureka.co/masters-progra... **
This Edureka session on Applications Of Artificial Intelligence will help you understand how AI is impacting various domains such as banking, marketing, healthcare and so on.
Following are the topics covered in this PPT:
AI In Artificial Creativity
AI In Social Media
AI In Chatbots
AI In Autonomous Vehicles
AI In Space Exploration
AI In Gaming
AI In Banking & Finance
AI In Agriculture
AI In Healthcare
AI In Marketing
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This document discusses the ethical issues surrounding artificial intelligence. It begins by noting humanity's long-standing fascination with creating tools that can replace human labor. However, others have warned of the potential harms of AI if not developed with wisdom. The document then outlines some of the common fears associated with AI, such as technology becoming autonomous and reversing the master-servant role between humanity and our creations. It also examines themes from Frankenstein that continue to emerge in science fiction, such as the ambiguity of technology and whether it will ultimately benefit or hinder humanity. The document considers various impacts that highly advanced AI could have, such as economic and educational impacts, and concludes by emphasizing the importance of considering whether just because we can
Chat GPT 4 can pass the American state bar exam, but before you go expecting to see robot lawyers taking over the courtroom, hold your horses cowboys – we're not quite there yet. That being said, AI is becoming increasingly more human-like, and as a VC we need to start thinking about how this new wave of technology is going to affect the way we build and run businesses. What do we need to do differently? How can we make sure that our investment strategies are reflecting these changes? It's a brave new world out there, and we’ve got to keep the big picture in mind!
Sharing here with you what we at Cavalry Ventures found out during our Generative AI deep dive.
What Is The Artificial Intelligence Revolution And Why Does It Matter To Your...Bernard Marr
AI (Artificial Intelligence) is the most powerful technology humans have ever had access to. AI is going to revolutionize the world of business and society at large. In this article, we take a look at this AI revolution.
This presentation provides an overview of artificial intelligence (AI), including its definition, introduction, foundations, advantages, applications, and limitations. AI is defined as the intelligence demonstrated by machines and the branch of computer science which aims to create intelligent agents. The presentation traces the foundations of AI through various fields such as philosophy, mathematics, neuroscience, and computer engineering. It also outlines the advantages of AI, such as reducing errors and exploring new possibilities, and the potential disadvantages like overreliance on AI and job losses. The presentation concludes that while AI tools can help solve problems, they cannot replace human capabilities.
10 Wonderful Examples Of Using Artificial Intelligence (AI) For Good Bernard Marr
Artificial intelligence is being used in many positive ways to help address societal problems. Some examples discussed in the document include using AI in cancer screening and healthcare decision-making, saving bee populations by analyzing sensor data, creating apps to help people with disabilities, addressing climate change through climate modeling, aiding wildlife conservation efforts, combating world hunger through crop analysis, reducing inequality by correcting algorithmic bias, identifying "fake news", improving medical imaging analysis, and prioritizing infrastructure upgrades.
Conversational AI versus AI Data ScienceRazorthink
Uncover the difference between a conversational AI and AI data science in this informative presentation, as well as how AI is being used on the Enterprise level.
This presentation provides an introduction to artificial intelligence (AI), its applications, and risks. It defines AI as the ability of machines to perform tasks typically requiring human intelligence, such as understanding language, recognizing patterns, and making decisions. Applications of AI discussed include machine learning, improving efficiency in industries, and using AI in healthcare. Risks covered are potential for biased outcomes from training data, malicious use of AI, and advanced AI surpassing human intelligence. The presentation concludes it is important to consider both the benefits and risks/unintended consequences of AI development and deployment.
This document provides an overview of artificial intelligence (AI) including its history and key concepts. It discusses how philosophers like Hobbes and mathematicians like Boole laid the foundations for AI by exploring symbolic logic and operations. Landmark developments included Babbage's analytical machine, Turing's universal machine concept, and McCarthy coining the term "artificial intelligence". The document also outlines branches of AI like natural language processing, computer vision, robotics, problem solving, learning, and expert systems. It provides examples of applications and concludes by noting progress made in creating human-like artificial creatures remains limited.
Title: Incredible developments in Artificial intelligence which was the future scenario.
Here I discussed the with the major backbones of AI (Machine learning, Neural networks) types Machine learning and type of Artificial intelligence and with some real-time examples of AI and ML & Benefits and Future of AI with some pros and Cons of Artificial Intelligence.
This talk gives an introduction about Healthcare Use cases - The AI ladder and Lifestyle AI at Scale Themes The iterative nature of the workflow and some of the important components to be aware in developing AI health care solutions were being discussed. The different types of algorithms and when machine learning might be more appropriate in deep learning or the other way will also be discussed. Use cases in terms of examples are also shared as part of this presentation .
9 Examples of Artificial Intelligence in Use TodayIQVIS
Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans.
Industry analysts argue that artificial intelligence is the future – but if we look around, we are convinced that it’s not the future – it is the present. The given examples will explain the true meaning and context.
Read as a blog post here. http://www.iqvis.com/blog/9-powerful-examples-of-artificial-intelligence-in-use-today/
This whitepaper provides an overview of artificial intelligence (AI) and its commercialization. It discusses the history and development of AI from early pattern recognition (AI 1.0) to today's deep learning (AI 2.0) to the emerging contextual reasoning (AI 3.0). Key points include how transfer learning and increased computing power are driving new AI applications and how AI is being applied commercially in healthcare, manufacturing, logistics, and other industries. The document also addresses the global demand for AI talent and the challenges of developing reliable AI systems that can operate under changing conditions.
Artificial intelligence involves using machines to simulate human intelligence through techniques like machine learning and deep learning. The presentation traces the origins of AI back to 1956 with the goal of giving computers abilities like reasoning, understanding language, and perceiving the world. Opportunities for AI include autonomous delivery vehicles, personalized shopping experiences using computer vision, using robots for tedious tasks, and developing new drugs. The winners in AI will be those focusing on narrow domains with vast data and those able to achieve network effects by crowdsourcing or leveraging multiple data sources.
An overview of artificial intelligence from the perspective of a potential venture capital investment: what it is, its history, how it can be used, and what it could mean for the future of various industries and humanity.
The document summarizes a panel discussion on the future of work from four different perspectives. It provides an overview of ISSIP (Institute for Service Innovation and Partnership), its programs and partnerships with organizations like IBM to explore topics related to the future of work through surveys, conferences and publications. Specific examples discussed include a past discovery summit on the future of jobs in an age of augmented intelligence held in 2018 and an upcoming summit on AI models for digital health.
This document provides an overview of artificial intelligence (AI) including definitions of different types of AI, a brief history of AI, potential application fields and use cases, and the future outlook for AI. It defines AI as ranging from everyday applications to self-driving cars. It discusses narrow AI, general AI, and superintelligence. The document also summarizes key milestones in the development of AI from 1955 to the present and potential opportunities and challenges of AI including automation, ethics, and politics. It provides examples of Austrian AI startups and their technologies. The outlook suggests that human-level AI may be achieved by 2040 and superintelligence by 2060 with impacts on robotics, climate change, human enhancement, and autonomous
Working with data is a challenge for many organizations. Nonprofits in particular may need to collect and analyze sensitive, incomplete, and/or biased historical data about people. In this talk, Dr. Cori Faklaris of UNC Charlotte provides an overview of current AI capabilities and weaknesses to consider when integrating current AI technologies into the data workflow. The talk is organized around three takeaways: (1) For better or sometimes worse, AI provides you with “infinite interns.” (2) Give people permission & guardrails to learn what works with these “interns” and what doesn’t. (3) Create a roadmap for adding in more AI to assist nonprofit work, along with strategies for bias mitigation.
The document announces an upcoming AI and OpenPOWER meetup on March 25th, 2018 in San Ramon, California from 4-7:30pm where attendees can learn about the latest advances in artificial intelligence and deep learning tools from industry leaders and pioneers and discuss how these technologies are impacting their industries. Prominent speakers will discuss topics ranging from machine learning performance and best practices to AI research at NASA and scalable machine learning with Apache SystemML on Power systems. The meetup aims to gather cutting-edge insights on AI from innovators across different sectors.
The document announces an AI and OpenPOWER meetup to take place on March 25th, 2018 from 4-7:30pm at the h2o.AI headquarters in Mountain View, CA. The meetup will feature prominent speakers from industry, research, and the financial sector who will discuss advances in deep learning tools and techniques. Key speakers include Ganesan Narayanasamy from IBM who will discuss OpenPOWER activities and supercomputers, and Sudha Jamthe from IoTDisruptions.com who will discuss AI trends towards a driverless world.
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
Artificial intelligence is promising new technologies but also hype that needs separating from reality. A discussion was held between executives in healthcare, machine learning and analytics with experts Hilary Mason and Sandy Allerheiligen. In the short term, AI automates tasks to save money and makes recommendations. In the long term, AI will transform industries like healthcare through medical imaging analysis and self-driving cars. Companies should start with problems not solutions, emphasize how AI augments not replaces humans, and engage skeptics to gain support.
Allaboutailuminarylabsjanuary122017 170112151616Quang Lê
Artificial intelligence is promising new technologies but also hype that needs separating from reality. A discussion was held between executives in healthcare, machine learning and analytics with experts Hilary Mason and Sandy Allerheiligen. In the short term, AI can automate tasks to save money and make recommendations. In the longer term, AI will transform industries like healthcare through medical imaging analysis and self-driving cars. Companies should start with problems not solutions, emphasize how AI augments not replaces humans, and engage skeptics to gain support.
Presentations from the AI conference held by EiTESAL that showcases the challenges met in the AI market in Egypt. presented by eng. Ahmed Hassaan from Orange.
Principles of Artificial Intelligence & Machine LearningJerry Lu
Artificial intelligence has captivated me since I worked on projects at Google that ranged from detecting fraud on Google Cloud to predicting subscriber retention on YouTube Red. Looking to broaden my professional experience, I then entered the world of venture capital by joining Baidu Ventures as its first summer investment associate where I got to work with amazingly talented founders building AI-focused startups.
Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.
Using the following deck, we sought to:
- define artificial intelligence and describe its applications in business
- decode buzzwords such as “deep learning” and “cognitive computing”
- highlight analytical techniques and best practices used in AI / ML
- ultimately, educate future AI leaders
The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.
With its focus on quantitative rigor, Wharton already feels like a perfect fit for me. In the next two years, I look forward to engaging with like-minded people, both in and out of the classroom, sharing my knowledge about AI with my peers, and learning from them in turn. By working together to expand Penn’s reach and reputation with respect to this new frontier, I’m confident that we can all grow into next-generation leaders who help drive companies forward in an era of artificial intelligence.
I’d love to hear what you think. If you found this post or the deck useful, please recommend them to your friends and colleagues!
Artificial intelligence, machine learning, and deep learning are related concepts in the field of artificial intelligence. Machine learning is a subset of AI that uses algorithms to learn from data and make predictions without being explicitly programmed, while deep learning is a specific type of machine learning that uses neural networks. The document provides definitions and examples of these concepts to help explain the differences between them.
This document is a project report on the topic of artificial intelligence and whether it is a boon or bane. It includes an introduction on AI, a brief history of AI, the importance and features of AI, as well as the advantages and disadvantages. The report discusses findings from the study, suggestions, the objective and methodology. It concludes that AI could potentially threaten humanity if its social impacts are ignored and not properly addressed through policy frameworks.
Artificial intelligence, particularly machine learning, is poised to have a major transformational impact on business. While AI is already used by thousands of companies, most big opportunities have yet to be tapped. Machine learning systems have achieved superhuman performance in areas like image recognition, speech recognition, and games like Go. However, their capabilities are still narrow - they have mastered specific tasks but lack general intelligence. The most progress has been in supervised learning, where systems are trained on large datasets with labeled examples to predict outputs. As more data and computing power become available, the potential for machine learning to automate tasks and transform industries will increase dramatically in the coming decade.
This document discusses Microsoft's efforts in artificial intelligence and machine learning. It provides context on the current state of AI, highlighting how machine learning has progressed from addressing specific tasks to becoming more general. It outlines Microsoft's investments in AI, including forming a new 5,000-person division and making AI pervasive across its products. The document also discusses challenges around developing machine learning programs and ensuring AI is developed in a responsible, trustworthy manner.
Generative AI: Redefining Creativity and Transforming Corporate LandscapeOsaka University
The advent of Generative AI is redefining the boundaries of creativity and markedly transforming the corporate landscape. One of the pioneering technologies in this domain is the Reinforcement Learning from Human Feedback (RLHF). Combined with advancements in LLM (Language Model) has emerged as a notable player. LLM offers two primary interpretations: firstly, as a machine capable of generating highly plausible texts in response to specific directives, and secondly, as a multi-lingual knowledge repository that responds to diverse inquiries.
The ramifications of these technologies are widespread, with profound impacts on various industries. They are catalyzing digital transformation within enterprises, driving significant advancements in research and development, especially within the realms of drug discovery and healthcare. In countries like Japan, Generative AI is heralded for its potential to bolster creativity. The value generated by such AI-driven innovations is estimated to be several trillion dollars annually. Intriguingly, about 75% of this value, steered by creative AI applications, is predominantly concentrated within customer operations, marketing and sales, software engineering, and R&D. These applications are pivotal in enhancing customer interactions, generating innovative content for marketing campaigns, and even crafting computer code from natural language prompts. The ripple effect of these innovations is palpable in sectors like banking, high-tech, and life sciences.
However, as with every innovation, there are certain setbacks. For instance, the traditional business model of individualized instruction, as seen in the context of professors teaching basic actions, is on the brink of obsolescence.
Looking ahead, the next five years pose pertinent questions about humanity's role amidst this technological evolution. A salient skillset will encompass the adept utilization of generative AI, paired with the discernment to accept or critique AI-generated outputs. Education, as we know it, will be reimagined. The evaluative focus will transition from verifying a student's independent work to gauging their ability to produce content surpassing their AI tools. Generative AI's disruptive nature will compel us to re-evaluate human value, reshaping the paradigms of corporate management and educational methodologies
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
2. INTRODUCTION
“As leaders, it is incumbent on all of us to
make sure we are building a world in which
every individual has an opportunity to thrive.
Understanding what AI can do and how it fits
into your strategy is the beginning, not the
end, of that process.”1
- Andrew Ng, former VP & Chief Scientist of Baidu, Co-
Chairman and Co-Founder of Coursera, the former
founder and lead of Google Brain, and an Adjunct
Professor at Stanford University.
2
1 Ng, A. (2019). “Andrew Ng: What AI Can and Can’t Do.” [online] Harvard Business Review.
Available at: https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now MM.DD.20XX
CONTENTS
I.
Introduction
II.
What is AI?
III.
What is ML?
IV.
Why now?
V.
Applications & Limitations
4. INTRODUCTION
MM.DD.20XX4
1.
AI & ML Overview
2.
Machine Learning: Part One
3.
Machine Learning: Part Deux
4.
AI: Political Economy
5.
AI: Safety & Ethics
6.
AI: Who’s Who and What’s Next?
5. WHAT IS AI?
“We define [AI] as the study of agents that
receive percepts from the environment and
perform actions. Each such agent
implements a function that maps percept
sequences to actions …”2
- Stuart J. Russell, Professor of Computer Science
University of California, Berkeley; and Peter Norvig,
Director of Research, Google Inc.; 1995
5 MM.DD.20XX2 Russell, S. J., Norvig, P., (1995) “Artificial intelligence: a modern approach.”
CONTENTS
I.
Introduction
II.
What is AI?
III.
What is ML?
IV.
Why now?
V.
Applications & Limitations
6. WHAT IS AI?
MM.DD.20XX6
AI
Machine
Learning
Neural
Networks
Deep
Learning
“Software that can make decisions and act autonomously”3
4 Koch, 2016. “How the Computer Beat the Go Master,” 20.
“The methods underlying AlphaGo … have huge implications
for the future of machine intelligence”4
3 Professor Nello Cristianini, speaking at Instant Expert AI, Dec 2019
Computer
Vision
Robotics
NLP
7. WHAT IS ML?
“Alpha Go isn’t just an expert system built
with handcrafted rules … instead it uses
general machine learning techniques to
figure out for itself how to win at Go”5
- Demis Hassabis, Founder & CEO of Google DeepMind;
January 2016
7 5 https://ai.googleblog.com/2016/01/alphago-mastering-ancient-game-of-go.html MM.DD.20XX
CONTENTS
I.
Introduction
II.
What is AI?
III.
What is ML?
IV.
Why now?
V.
Applications & Limitations
8. WHAT IS ML?
MM.DD.20XX8
Data Algorithm Output
Validation
Check of how accurate
the result is
Use by user
Additional data point
recalibrates the
algorithm
Output data is fed
back into data source
10. WHAT IS ML?
MM.DD.20XX10
K-Means
ClusteringDecision Trees
&
Random
Forests
Principal
Component
Analysis
(PCA)
Linear &
Logistic
Regression
Isolation
Forests
Sentiment
Analysis
Reinforcement
Learning
Artificial
Neural
Networks
Convolutional
Neural
Networks
11. WHY NOW?
“[…] in no part of the field have discoveries
made so far produced the major impact that
was then promised”6
- Sir James Lighthill, in an evaluation of AI research
compiled for the British Science Research Council in
1973. It is held that this paper led to the “AI Winter”
when funding for research in this area dried up.
11
6 James Lighthill (1973): "Artificial Intelligence: A General Survey" in Artificial
Intelligence: a paper symposium, Science Research Council
MM.DD.20XX
CONTENTS
I.
Introduction
II.
What is AI?
III.
What is ML?
IV.
Why now?
V.
Applications & Limitations
13. WHY NOW?
MM.DD.20XX13
MOORE’S LAW MORAVEC’S PARADOX
AMARA’S LAW
“We tend to overestimate the
effect of a technology in the
short run and underestimate
the effect in the long run”8
Computing performance
doubles every 18 months
“It is comparatively easy to make
computers exhibit adult level
performance in solving problems on
intelligence tests or playing
checkers, and difficult or impossible
to give them the skills of a one year-
old when it comes to perception
and mobility.”
8 C. Frey, 2019, The Technology Tray: Capital, Labor, and Power in the Age of
Automation (Princeton, NJ: Princeton University Press), 323.
9 H. Moravec, 1988, Mind Children: The Future of Robot and Human Intelligence
(Cambridge, MA: Harvard University Press), 15.
14. APPLICATIONS &
LIMITATIONS
“The danger is that if we invest too much in
developing AI and too little in developing
human consciousness, the very sophisticated
artificial intelligence of computers might only
serve to empower the natural stupidity of
humans.”10
- Yuval Noah Harari, 21 Lessons for the 21st Century
14 10 Yuval Noah Harari, 21 Lessons for the 21st Century MM.DD.20XX
CONTENTS
I.
Introduction
II.
What is AI?
III.
What is ML?
IV.
Why now?
V.
Applications & Limitations
AI is being described as the General Purpose Technology of our time, set to displace up to 47% of jobs in the next 15 years. But how much do you really know about AI and how it works? My name’s Charlotte and I’ve just returned to Accenture having spent a year on Leave of Absence to learn about Big Data, Machine Learning and AI. Today I want to introduce the first episode of a 6 part series to share with you some of what I’ve learned.
I want to kick off this presentation with an important quote from Andrew Ng. I believe that we are all in a position to help build a world where every individual has an opportunity to thrive. I hope that today I can help you to understand what AI can do, and what it cannot, to help you drive positive change.
So by way of introduction I want to do 2 things. Firstly I want to introduce myself, and secondly introduce why I’m giving this talk.
My name is Charlotte and I’ve been with Accenture for 4 years. I joined straight after university having studied PPE (Politics, Philosophy & Economics) with Mandarin. I was part of H&PS for 3 years before taking a sabbatical. In my last 18 months with the company I was a solution design architect on a Student Lifecycle Project, which touched on analytics and Big Data. I was fascinated by the seemingly unlimited possibilities of these technologies. However, having no background in computer science or statistics, I couldn’t get a foot in the door to learn more. And as good as MyLearning is, learning an entirely new discipline requires more in-depth resources, as well as time and focus.
So I found a “Master’s in Big Data and Management” taught at a university in Rome – if I’m going to study something difficult I want to study somewhere warm with good wine. I’m very grateful to everyone who supported and enabled me to take a leave of absence. As well as learning a huge amount, I can honestly say that it was the best year of my life, so thank you.
During my studies I became very interested in AI on both a technical and philosophical level. This presentation series is a summation of everything I’ve learnt – from lectures, books, conferences, blog posts, Netflix documentaries – you name it. And I want to share it with you. I want to share it with you because I have had the luxury of time and freedom to explore this topic; and I know that there are many of you who are interested in knowing more about this topic but perhaps don’t know where to start.
This is the first episode in a 6 part series. This series is designed to be a good overview to help you understand what AI is and what it isn’t. To dispel some myths, help you speak with more confidence (both to clients and to your friends) about AI, and help you understand and work better alongside data scientists and AI/ML engineers.
A quick note on what this series isn’t. This isn’t going make you a data scientist. This isn’t comprehensive. And whilst some code will be introduced, I do not expect you to know how to code (or even read code). I will explain it line-by-line as and when it comes up; it’s just important that you see what’s actually happening and that ML isn’t ‘magic’. Unfortunately, it is still just computers computing.
The best place to start with when explaining a topic is to find the right definition. There are many definitions of Artificial Intelligence, but the one quoted here stands up best to scrutiny. AI is the study of agents that receive percepts (that is, an input ‘perceived’ by the agent) from the environment and perform actions. Each such agent implements a function that maps percept sequences (that is, multiple observed inputs) to actions.
This definition reflects what we understands computers to do – receive inputs, perform computations, and produce outputs. The computations used in AI relate to ‘statistical inference’ – just in case you were interested.
But this definition makes AI sound so . . . simple. ‘Input, output’ doesn’t sound so complicated. So what is it that makes AI seem so special? At a recent AI focused event hosted by New Scientist, Professor Nello Cristianini described AI as “software that can make decisions and act autonomously”. This addition of the word “autonomous” makes a difference, this is where AI starts to get complicated, where, unlike rule-based programming, we can’t see exactly how it works.
Alongside this addition of acting autonomously, for a tool to be considered artificially intelligent, it must also be able to learn. Machine learning theories have been around for many years; however it’s only been in the last decade that we’ve had the computing resources to enable such approaches to perform well. This move away from rule-based programming, towards machine learning in the 21st century, enabled machines to emulate human intelligence, as reflected in AlphaGo’s defeat of the world’s best Go player in 2016. I recommend watching the related Netflix documentary, where you watch as AlphaGo plays some moves no human would make, that ultimately lead to victory.
What is important to know is that AI is no one single technology or methodology, it is instead a collection of technologies. When you get most introductions of AI you usually see a diagram that looks something like this. This diagram shows you how ML, DL and NN interplay. But I’d like to add more to this. The field of AI also comprises areas of research into computer vision, robotics and neural linguistic programming. These all utilise different elements of machine learning, but exist as stand-alone technologies and areas of research. The collection of all of these fields fall under AI.
So I’ve talked about the importance of machine learning as part of AI, but what actually is ML? In essence, ML is the ‘secret sauce’ which has pushed AI back into the forefront of computing research and development. Previously, so-called ‘intelligent’ algorithms, such as DeepBlue which beat the world chess champion Garry Kasparov in 1996, relied on rule-based programming. This means that the rules are known, and every move can be coded. Therefore, when faced with a particular chess board, the program can run every conceivable move from that point, and play the move which has the highest chance of leading to a win.
However, the program used for DeepMind is completely unusable for any other application. It has been told how to play chess and that is what it can do. It cannot ever understand how to play a different game, not without re-programming the program from the ground up.
Google’s DeepMind however tried a different approach when creating an AI which could learn any game. Instead of programming each step of a game, it told the algorithm the rules of the game and had it play against itself hundreds of thousands of times in order for it to learn the best game strategies. Each time the machine played, it took the strategy and outcome of the last game and used it as an input to devise its next strategy. This is machine learning.
So how does a machine learn? It is, after all, not a human being, and it does not have the same inner workings as the human mind, as much as researchers are trying to get computers to mimic the human brain. Before we dive into the different types of machine learning and algorithms, let’s start at a super high level.
All machine learning starts with data. This could be spreadsheets, text files, photos, videos . . . Anything stored in a digital format. For now, let’s stick with the idea of spreadsheets. The data points on these spreadsheets are fed into an algorithm, which has been coded by a data scientist (and we’ll get into those later on). This algorithm runs its computations against all of the data points in the spreadsheet and spits out an output. This output can again be in many different formats, and there’s super interesting research into text-generation, photo and video manipulation in this area. More often than not the output will be a number or a decision code, essentially a label. Once you have the output, or labels, this can then be used by the end-user, or it can be fed into further automations to trigger various actions. The output is then fed back into the algorithm and this is crucially where the learning happens. By validating the output generated by the algorithm against the actual result (prediction versus reality), the algorithm can be adjusted to be more accurate in future. The output data is then fed back into the data source to be an additional data point for the algorithm in future. It is this cyclical nature of machine learning which separates it from previous rule-based programming.
Machine learning approaches can generally be split into two categories. The first is ‘supervised learning’. This is where we have a data set in which the “right answers” are already given – or ‘labelled data’. For example we may have a data set which gives us information about houses – size, number of bathrooms, garden yes/no and the sale price of that house. We can use that data set to help train an algorithm where we can learn which variables (size, number of bathrooms etc.) have the most impact on our independent variable – house prices. Once the algorithm is trained, we can then use our program to help us predict future house prices.
In unsupervised learning however we do not have labels for our data. In this scenario the machine will look for patterns and will try to group similar data points based on similar factors. We can tell our algorithm how many groups we’re looking for, or we can let the machine decide for itself how many distinct groups there are. For example we could present a data set describing various fruits: colour, weight, size; and we ask our algorithm to tell us how many different fruits there are, or how many of each type there are.
There is a third way and this is reinforcement learning, which will be covered more in the second deep dive on Machine Learning. In business settings, supervised learning is the most commonly used approach, with some uses of unsupervised learning being applied. Reinforcement learning can be used, but this is still being explored in academic fields, and you won’t see it as much being used by businesses.
There are many different types of Machine Learning methods, each suited to different tasks. This is by no means an exhaustive list, but just to give you a sense of the variety and breadth out there. It is the job of a data scientist to know which algorithm is best to build an accurate model. In fact, when building a model, data scientists may try many different approaches to find the one with the highest accuracy.
In blue we have our supervised learning algorithms: linear & logistic regression, decision trees and random forests and sentiment analysis. These are best used in cases when have a regression or a classification problem. A regression problem is when you want to know numbers, such as, given details about a house on the market, how much would you predict it is sold for? A classification problem is when you want to classify items, such as, given details of someone applying for a loan, will they or won’t they default on the repayments? Or, as in the case of sentiment analysis, is this tweet positive or negative?
In deep purple we’ve got our unsupervised learning algorithms. These help us achieve a number of different goals. Firstly we can use unsupervised learning to help us cluster the data, such as with K-Means clustering. This is different from categorisation because rather than feeding in the pre-defined groups, we ask our algorithm to find groups on its own, which might reveal categories we hadn’t previously been aware of. We can also look to reduce the dimensions, or complexity, of our data using Principal Component Analysis. And finally we can look for anomalies in our data using the Isolation Forest algorithm.
Neural networks are advanced enough to be used with labelled or unlabelled datasets, hence the rather hideous combination of colours there. Reinforcement Learning is out on its own as, as mentioned before, it is its own beast and plays to a different (or given) set of rules.
All of these approaches are different ways for a machine to learn from data and produce statistical inferences, which can then be used to drive decisions. I’ll be covering the basics of how these algorithms work in the two sessions on Machine Learning in this series.
AI is a field of research which has been around for the last 70 years, so it begs the question – why are we starting to hear so much about it now? I wanted to start this section with this quote from Sir James Lighthill. In 1973 he was commissioned by the British Science Research Council (which has since morphed into the UK Research and Innovation organisation), to evaluate the state of AI research in the UK. And this is what he came back with – “in no part of the field have discoveries made so far produced the major impact that was then promised”. Almost overnight funding for AI research was pulled in all but two universities, and it is widely held that this led to the first ‘AI winter’ where there was a dearth of research or progress in the field.
We can see this AI Winter in this excellent diagram of the history of AI I found during my research – I can’t claim credit for it unfortunately. There have been two periods of excitement, followed by ‘winters’ of limited progress. As we can see we’re currently in an era of explosive growth, and whilst there are those who would warn you that we are about the dip into another AI winter, there are others who believe we have hit the ‘eternal spring’. (I’ll be covering this in the episode on AI & Political Economy), If the current trend is one of growth and adoption, what’s changed?
I want to introduce you to three key concepts, all of which are at play in regards to AI. These can be attributed to other areas of research and technology, so they’re good to know, and at the very least might help you at a pub quiz one day.
You’ve most likely heard of the first law, if not by name but by nature. Amara’s Law states that: “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run”. Amara’s law draws out the hurdles to adoption of new technologies. Implementing a new technology requires many changes to take place prior to adoption. In the case of AI, organisations need to move from paper-based, manual processes to digital technologies before they can create the data pipeline necessary to support AI. And all of that takes time, money and resources. We are now starting to see organisations that are reaching the end of their digital transformation who are now in a position to take advantage of AI/ML technologies.
The second law will again be a familiar one to many of you – Moore’s law is an empirical observation that computing performance doubles every 18 months. Computing power is incredibly important in executing complicated algorithms against huge datasets. It has taken time for such computing power to be available. Now with more compute power, and the ability to execute code across multiple processors in the cloud, churning through the massive or real-time datasets required for AI is quicker and cheaper, reducing the limitations of time and cost to implementing such technologies.
The final element in this triangle is Moravec’s Paradox – perhaps lesser known than the other two. Moravec’s Paradox said that “It is comparatively easy to make computers exhibit adult level performance in solving problems on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one year-old when it comes to perception and mobility.” This paradox still holds true. There are things which computers are simply better at doing – computing. But many activities are still very limited to the realm of human capabilities – walking upstairs, dexterity, empathy, creativity. For anyone needing reassurance that The Terminator isn’t going to be knocking on their door anytime soon, look up ‘robot fails’ on YouTube and have a good laugh. Moravec’s Paradox was written in 1988, and it alludes to the challenge in capturing tacit knowledge. For example, can you describe, in minute detail, how to drive? There is a huge amount of information that has to be learnt in order to know how to drive. Without being to articulate that knowledge, rule-based programming was unable to create programs which could successfully drive in complex situations. However, with the move towards machine learning algorithms that imitate how humans learn, machines can accumulate that tacit knowledge for themselves. This has vastly increased the variety of situations in which AI technologies can be applied.
Therefore, as more organisations reach digital maturity, computing power increases and machines are able to learn tacit knowledge, we are starting to see AI technologies become increasingly prevalent.
So let’s look at the areas where AI & ML is most applicable, and importantly, where it is not.
There are so many applications of AI & Machine Learning. This technology is being applied across so many industries, from the finance sector taking advantage of forecasting models, to logistics companies using machine learning to direct its deliveries. Autonomous driving will take some years to hit to mainstream, but self-driving vehicles are already in operation across Amazon warehouses to stack shelves and pick products.
I could take you through a list of examples where companies are using these technologies in innovative new ways, but what I want to do is give you a general idea of what machine learning can do. I hope this will help you to recognise opportunities where this could be applied with your clients.
Firstly anomaly detection is useful for understanding fraudulent activities, or cyber security risks.
NLP is brilliant for translating documents, chatbots, sentiment analysis and engaging with customers. NLP can be used to make sense of masses of complex reports, generating simple summaries, or even creating original documents.
Moving onto pattern recognition, this is really well applied in facial recognition, object recognition and forecasting and prediction. Forecasting works not only for financial data, but also for customer footfall, call centre calls, the chance of getting a space in the shopping centre car park at 4:45pm on Christmas Eve . . .
Gamification also uses pattern recognition. Be it an educational platform or a performance management scheme, being able to recognise where players are struggling and need extra assistance is a job for pattern recognition. I know I said I wouldn’t give you a list of examples, but just the one - Duolingo uses this approach to understand which particular words language learners struggle with, and how frequently words should come up in their games in order for linguists to remember them.
Gamification does also fall partially under the theme of recommendation systems, which can be used to drive personalisation. Recommendation systems do exactly what they say on the tin and recommend products or services to customers, but they can also be used to recommend next best actions to staff, or used in personalised healthcare.
This is a very quick whip through of the general themes where AI and machine learning can be applied. I really hope that a couple of these have connected with you and you can think of a couple of cases with your clients where these could be applied.
So now you’ve got your million dollar idea, let me tell you why it won’t work. No, just kidding. But in all seriousness, there are some things you need to think about before you put this in front of your client.
Firstly, in designing your solution look at the technology. I started this presentation by saying that I thought that Data Analytics was this seemingly unlimited world. Now, in reality, there are limits to what the tools can do. Creative, empathetic AI is confined to research labs at the moment, as are back-flipping robots. Start by looking for cases where similar solutions have been used. Or talk to a data scientist, they’ll be well placed to tell you whether the technology for what you want to achieve exists.
Secondly, whilst your solution may work in principal, building a sustainable data pipeline is an uphill battle. Firstly, ensuring the data exists in a useable format is paramount. Perhaps the data you intend to use is in a legacy system that’s hard to extract from, or perhaps its even on paper, or in team member’s heads. How will you get access to this data? You will need to ensure that the data infrastructure required by your tool is available.
Okay, say the data exists, and in a useable format that you’ve got access to, but do you have the right skills to be able to build the data pipeline? Data collection, cleaning, analysis, modelling and tool deployment all require their own skills, pretty much all of which are in short supply at the moment. Does your client have people with these skills? Do they need to upskill their workforce or make new hires? How quickly can they do this? These are all important factors defining the success your solution will have.
Fourthly, you must check that you are adhering to the law in your AI solution. The key law you absolutely need to be aware of GDPR. There are some really useful primers out there that take all of ten minutes to read and are really helpful. I would encourage you to familiarise yourself with these.
Finally, everything is underpinned by trust. If you do not have the trust of your customers, your staff, the public, you are risking the reputation of your client. There is a lot of fear around AI technologies. Some of it is fear of change. But there is a lot of very reasonable fear around how unexplainable the technology is, the ‘black boxes’ which are increasingly dictating our lives. There is fear around how little is known about it and how unregulated it is. And there is fear around how it will change the landscape of the labour market, and how many are jobs are predicted to be lost to machines. Addressing these fears is paramount to the success of AI, and we must make careful decisions about how we chose to use AI. I will be covering these issues and more in the sessions on AI & Political Economy, and AI: Safety & Ethics. We are all in a position to influence the decisions we make in regards to AI so I really encourage you to inform yourselves.
Luciano Floridi, Oxford University Professor of Philosophy recently said, the decisions we make in the short run dictate the long run. Don’t you wish we’d made better decisions about energy policy fifty years ago?
I didn’t mean to end on a note of doom and gloom, but I think it’s important to be aware that just because you have a hammer, it doesn’t make everything a nail. AI and ML are already changing the world we live in, and I truly believe in its power to change the world for good. It’s why I have spent the last year learning about these topics and why I am so excited to share what I’ve learnt with you. I’m happy to take questions now, or do feel free to reach out to me afterwards. Thank you, and good luck on your own AI journeys!