Overview

Evisort, an artificial intelligence (AI) technology company, today announced the appointment of legal tech veteran Alexander Su (Alex) as its Director of Business Development. Su previously led the sales team at Logikcull, an eDiscovery automation company that grew revenue from 0 to $10M in just 19 months. Su's move is the latest in a recent trend of leadership talent being drawn to Evisort due to its explosive growth this year. Over the past decade, Su has been uniquely positioned to observe the evolution of legal technology. Earlier in his career, Su managed numerous discovery workflows as an associate at Sullivan & Cromwell.

Why are deep neural networks hard to train? Appendix: Is there a simple algorithm for intelligence? If you benefit from the book, please make a small donation. I suggest $5, but you can choose the amount. Thanks to all the supporters who made the book possible, with especial thanks to Pavel Dudrenov. In the last chapter we learned that deep neural networks are often much harder to train than shallow neural networks. That's unfortunate, since we have good reason to believe that if we could train deep nets they'd be much more powerful than shallow nets. But while the news from the last chapter is discouraging, we won't let it stop us. In this chapter, we'll develop techniques which can be used to train deep networks, and apply them in practice. We'll also look at the broader picture, briefly reviewing recent progress on using deep nets for image recognition, speech recognition, and other applications. And we'll take a brief, speculative look at what the future may hold for neural nets, ...

Previously I have written about the data science puzzle, an overview which defines a number of key concepts related to data science, and which which attempts to explain how these pieces relate to one another and fit together. This time we will take a similar look at the machine learning model. Keep in mind we will be approaching machine learning from a supervised perspective, and all concepts are discussed with classification as our goal throughout (though regression would be similar). Importantly, this puzzle view will not cover other machine learning paradigms, such as unsupervised learning and reinforcement learning, so keep that in mind as the pieces are unveiled. With that said, read on to find out how the machine learning puzzle comes together.

Previously I have written about the data science puzzle, an overview which defines a number of key concepts related to data science, and which which attempts to explain how these pieces relate to one another and fit together. This time we will take a similar look at the machine learning model. Keep in mind we will be approaching machine learning from a supervised perspective, and all concepts are discussed with classification as our goal throughout (though regression would be similar). Importantly, this puzzle view will not cover other machine learning paradigms, such as unsupervised learning and reinforcement learning, so keep that in mind as the pieces are unveiled. With that said, read on to find out how the machine learning puzzle comes together.

The Introduction gives an overview of artificial intelligence and its use in marketing, explains key terms, and sets the scene for following chapters. Here, we will bring you up to speed on what you need to know moving forward, whether you're new to the topic or an experienced digital marketer. How Does Marketing Software Use AI? This chapter provides an overview of how currently available AI systems can be deployed by purchasing commercial solutions. We look at what types of products are available and what they can do for your business.

Since a few years, chatbots are here, and they will not go away any time soon. Facebook popularised the chatbot with Facebook Messenger Bots, but the first chatbot was already developed in the 1960s. The chatbot was developed to demonstrate the superficiality of communication between humans and machines, and it used very simple natural language processing. Of course, since then we have progressed a lot and, nowadays, it is possible to have lengthy conversations with a chatbot. For an overview of the history of chatbots, you can read this article.

But attempting to model an intelligence after either the ephemeral human mind or the exact physical structure of the brain (rather than iterating increasingly capable Roombas) is no small task -- and with no small amount of competing hypotheses and models to boot. In fact, a 2010 survey of the field found more than two dozen such cognitive architectures actively being studied. The current state of AGI research is "a very complex question without a clear answer," Paul S. Rosenbloom, professor of computer science at USC and developer of the Sigma architecture, told Engadget. "There's the field that calls itself AGI which is a fairly recent field that's trying to define itself in contrast to traditional AI." That is, "traditional AI" in this sense is the narrow, single process AI we see around us in our digital assistants and floor-scrubbing maid-bots.

Artificial intelligence (AI) is quickly changing just about every aspect of how we live our lives, and our working lives certainly aren't exempt from this. Soon, even those of us who don't happen to work for technology companies (although as every company moves towards becoming a tech company, that will be increasingly few of us) will find AI-enabled machines increasingly present as we go about our day-to-day activities. From how we are recruited and on-boarded to how we go about on-the-job training, personal development and eventually passing on our skills and experience to those who follow in our footsteps, AI technology will play an increasingly prominent role. Here's an overview of some of the recent advances made in businesses that are currently on the cutting-edge of the AI revolution, and are likely to be increasingly adopted by others seeking to capitalize on the arrival of smart machines. Before we even set foot in a new workplace, it could soon be a fact that AI-enabled machines have played their part in ensuring we're the right person for the job.

Both AI and cybersecurity are broad and poorly understood fields. This book helps give you an overview of the various technologies that make up AI, where they have come from, and what AI has evolved into today. Cybersecurity is another field that has evolved over the last few decades. Dive into the world of cybersecurity and then learn how AI is being applied to the battle. When you're done reading this book, you will be spouting terms like cognitive computing, machine learning, and deep learning, and know how they apply to the cybersecurity space.

What is new about AI marketing is the accuracy you have when delivering and executing toward a specific target, the complete (or almost complete) automation of procedures and actions, and the high economic efficiency resulting from using AI in these marketing activities. This is yet another reason for marketing teams to leverage AI: If you don't, you risk falling out of sync with customers and losing the competitive edge as consumers turn to AI solutions as well.