In his recent book The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do, AI researcher Erik J. Larson defends the claim that, as things stand today, there's no plausible approach in AI research that can lead to generalized, human-like intelligence. It's important to understand what the author is claiming- and what he's not claiming. He's not claiming that computers can never think like humans, as some philosophers of mind have claimed. Rather, his position is- if there's indeed a way to make computers think like humans, we haven't the foggiest what that is. Our current approaches- no matter how promising they might seem- are all dead ends. He contrasts this with the prevailing optimism about AI: the perception that current approaches are on the path to generalized intelligence, and the problems of this approach are, at least in theory, solvable. Thought this way, human-like computers seem just a matter of time. Larson, on the other hand, argues that even the fundamental theoretical principles of current AI approaches are non-starters. All of the current approaches in AI (or at least the most promising ones) are based on a certain model of thinking: inductive inference.
"Ask forgiveness, not permission" has long been a guiding principle in Silicon Valley. There is no technological field in which this principle has been more practiced than the machine learning in modern AI, which depends for its existence on giant databases, almost all of which are scraped, copied, borrowed, begged, or stolen from the giant piles of data we all emit daily, knowingly or not. But this data is hardly ever rigorously sourced with the subjects' permission. "Because we can," two sociologists tell Kate Crawford in Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence, by way of acknowledging that their academic institutions are no different from technology companies or government agencies in regarding any data they find as theirs for the taking to train and test algorithms. This is how machine learning is made.
This chapter focuses on Deep Learning and techniques that can be used to keep neural networks from getting out of hand as their complexities get deeper. Traditionally Deep Learning is defined as a neural network that contains 3 or more layers. But, with this addition of layers comes additional complexity and with complexity comes more ways for a project to break. Most of this chapter deals with introducing us to the techniques that we can use to minimize these breakages when training deep models. Neural Networks are trained through backpropagation using gradient descent to adjust their weighting so that we get the intended result.
A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. In this mind-expanding book, scientific pioneer Marvin Minsky continues his groundbreaking research, offering a fascinating new model for how our minds work. He argues persuasively that emotions, intuitions, and feelings are not distinct things, but different ways of thinking. Introduction to Artificial Intelligence presents an introduction to the science of reasoning processes in computers, and the research approaches and results of the past two decades.
Many of you must be having question in your mind about best books to learn Data Science. Here is the list of books extremely useful for data science from various subject areas such as Mathematics and Statistics, Programming, Python, Machine Learning etc. These books are extremely useful if someone is learning Data Science from scratch or even if someone is working as Data Scientist for few years.
There is no dearth of sources when it comes to mastering machine learning. Now this is a problem too. Be it a book or a blog, beginners and those looking for a career transition might find it challenging to pick the right resource. So, we have asked machine learning practitioners for the right books to begin with to gain a comprehensive understanding of all things machine learning. Designing data intensive applications is one of the most widely read books by data engineers across the world.
'Tis the season to sit back, relax, and crack open a good book. Summer is right around the corner, which means it's time to get your summer reading lists in order once again. Not sure where to start? The editors at The Amazon Book Review are here to share some fantastic recommendations. On Wednesday, Amazon published its annual Best Books of the Year (so far) list, a carefully curated collection of impressive, engaging reads that published from January to June.
This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real life questions, to choose and apply appropriate methods to answer those questions, and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, prediction with machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by over 360 practice questions and 120 data exercises.
What you can't find in someone's voice, you might find in someone's writing. I was always more inclined to following and referring video tutorials/lectures whenever it comes down to studying something on my own from the web. I found it easier ( just like some of you) to understand and not go through the pain of reading available books. Most of the time, I felt the same unless recently I discovered those writers or publishers who eliminated the element of'bore' from subject books and made them so… much interesting. This started when one of my really smart friends told me to start reading books because they contain more content and adds to a really important skill for any person, that is reading and understanding.
Despite their overlapping interests, it is rare for developmental neuro biologists to consult artificial intelligence (AI) experts in the course of their research and vice versa. But in his new book, The Self-Assembling Brain, neurobiologist Peter Robin Hiesinger argues that doing so would likely be of great benefit to both parties. In 10 chapters, he describes a series of imagined conversations between four hypothetical individuals--a developmental geneticist, a neuroscientist, a robotics engineer, and an AI researcher--that offer readers insight into the information that is needed both to understand the workings of the brain and to create an artificial system that mimics the brain. These fictional conversations are followed by "seminars" in which the author discusses specific topics in greater detail. Hiesinger elegantly moves through a variety of topics, ranging from biological development to AI and ending with a discussion of the advances that deep neural networks have brought to the field of brain-machine interfaces.