If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
I chose artificial intelligence as my next topic, as it can be considered as one of the most known technologies, and people imagine it when they talk about the future. But the right question would be: What is artificial intelligence? Artificial intelligence is not something that just happened in 2015 and 2016. It's been around for a hundred years as an idea, but as a science, we started seeing developments from the 1950s. So, this is quite an old tech topic already, but because of the kinds of technology that we have access to today -- specifically, processing performance and storage -- we're starting to see significant leaps in AI development. When I started the course entitled, "Foundations of the Fourth Industrial Revolution (Industry 4.0)," I got deeper into the topic of artificial intelligence. One of the differences between the third industrial revolution -- defined by the microchip and digitization -- and the fourth industrial revolution is the scope, velocity and breakthroughs in medicine and biology, as well as widespread use of artificial intelligence across our society. Thus, AI is not only a product of Industry 4.0 but also an impetus as to why the fourth industrial revolution is currently happening and will continue to do so. I think there are two ways to understand AI: the first way is to try giving a quick definition of what it is, but the second is to also think about what it is not.
A field that is bringing alot of commotion and noise is Artificial Intelligence. But something that really fascinates me is a subset of that field known as Artificial General Intelligence (AGI) or the holy grail of Artificial Intelligence. Many of today's machine learning or deep learning algorithms would be classified as Artificial Narrow Intelligence (ANI). I believe many of these algorithms are rapidly proliferating at the back end of most technologies we currently use from ride-sharing apps to social media and to other applications. And I believe that will continue to happen at an exponential pace until many specific tasks can be done better by algorithms than by humans.
To not question things is to agree to stay in the same place. Often during that process, your mind can go to wrong directions. But still, you can learn a lot, during the exploration of uncharted territories of the human potential. Excuse me that I will move away from the main topic for a moment, but first I want to share something. More than 10 years ago I started to learn in a hard way what is the power of continuous effort.
Recent years have seen breakthroughs in neural network technology: computers can now beat any living person at the most complex game invented by humankind, as well as imitate human voices and faces (both real and non-existent) in a deceptively realistic manner. Is this a victory for artificial intelligence over human intelligence? And if not, what else do researchers and developers need to achieve to make the winners in the AI race the "kings of the world?" Over the last 60 years, artificial intelligence (AI) has been the subject of much discussion among researchers representing different approaches and schools of thought. One of the crucial reasons for this is that there is no unified definition of what constitutes AI, with differences persisting even now.
Sometimes these notebooks find their way into production, but their code and structure are often far from ideal. In this session, we cover some best practices around creating and operationalising notebooks. We will talk about structure, code style, refactoring in notebooks, unit testing, reproducibility and more. Nikolay Manchev is a machine learning enthusiast and speaker. His area of expertise is Machine Learning and Data Science, and his research interests are in neural networks with emphasis on biological plausibility. Nikolay was a Senior Data Scientist and Developer Advocate at IBM [masked]) and currently acts as the Principal Data Scientist for EMEA at Domino Data Lab. Talk 3: Generative Deep Learning - The Key To Unlocking Artificial General Intelligence by David Foster Generative modelling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavours such as painting, writing, and composing music. In this talk, we will cover: - A general introduction to Generative Modelling - A walkthrough of one of the most utilised generative deep learning models - the Variational Autoencoder (VAE) - Examples of state-of-the-art output from Generative Adversarial Networks (GANs) and Transformer based architectures.
Whenever we measure anything using a particular number system, the corresponding measurements will be constrained by the structure of that number system. If the number system has a different structure than the things we are measuring with it, then our measurements will suffer accordingly, just as if we were trying to force square pegs into round holes. For example, the natural numbers make lousy candidates for measuring lengths in a physics laboratory. Lengths in the lab have properties such as, for example, the fact that for any two distinct lengths, there is an intermediate length strictly between them. The natural numbers lack this property. Imagine the poor physicist, brought up in a world of only natural numbers, scratching his or her head upon encountering a rod with length strictly between two rods of length 1 and 2.
At the core of the discipline of artificial intelligence is the possibility that one day we'll have the option to construct a machine that is as smart as a human. Such a system is frequently alluded to as artificial general intelligence, or AGI, which is a name that recognizes the idea from the more extensive field of study. It additionally clarifies that true AI has insight that is both wide and flexible. Until this point in time, we've built innumerable systems that are superhuman at explicit tasks, yet none that can match a rat with regards to general mental ability. However, regardless of the centrality of this idea to the field of AI, there's little understanding among analysts with respect to when this feat might really be achievable.
Dr. Goertzel has published 20 scientific books and 140 scientific research papers and is the leading architect and designer of the OpenCog system and associated design for human-level general intelli… (show all) Dr. Goertzel has published 20 scientific books and 140 scientific research papers and is the leading architect and designer of the OpenCog system and associated design for human-level general intelligence. Goertzel co-authored "Artificial General Intelligence," published in 2002 by Springer Publishing. He is also the chair of the Artificial General Intelligence (AGI) conference series, advisor to Singularity University and former Director of Research of the Machine Intelligence Research Institute (formerly the Singularity Institute). He also served as Chief Scientist Officer for Hanson Robotics until early 2019.
It's important for stakeholders in strategic Artificial Intelligence (AI) projects to understand the difference between narrow AI and Artificial General Intelligence (AGI). We currently live in the age of narrow AI. These are highly specialized algorithms focused on specific tasks. Think playing Go or Chess; or the example in computer vision where algorithms are trained to detect things like cancer. The list of very specific tasks is far-reaching into many fields like finance, insurance, and medicine.
Say you've been driving on the roads of Phoenix, Arizona, all your life, and then you move to New York. Do you need to learn driving all over again? You just have to drive a bit more cautiously and adapt yourself to the new environment. The same can't be said about deep learning algorithms, the cutting edge of artificial intelligence, which are also one of the main components of autonomous driving. Despite having propelled the field of AI forward in recent years, deep learning, and its underlying technology, deep neural networks, suffer from fundamental problems that prevent them from replicating some of the most basic functions of the human brain.