Artificial intelligence (AI) has made its way into many aspects of our lives, even into toys for kids like Anki's Cozmo, which resembles a roboticized Ewok. But as things go, AI isn't just for devices; it's made and continues to make its way into eCommerce and is out there working to determine what to sell to you, how you shop and ensure you have a good shopping experience. According to Gartner, by 2020 85% of customer interactions will be managed without a human, and at the close of 2018, customer digital assistants will recognize customers by face and voice across channels. Investment-wise, in 2014 there were more than 300 million in venture capital invested in AI startups according to Bloomberg. Brands are on board and are using AI to build smarter platforms they hope will create a better online shopping experience for the consumer.

This month, US home improvement retailer Lowe's will introduce the LoweBot – an artificially intelligent (AI) robot – into 11 of its San Francisco Bay stores. Tapping into the rising influence of AI in all service industries, the 5ft tall robot, developed by Silicon Valley technologists Fellow Robots, has been commandeered to ease the customer journey in-store. This frees up (human) sales staff for more complex, nuanced and creative brand-consumer connections. The LoweBot uses 3D scanning tech to detect the human body, prompting it to greet customers. It's also equipped with speech-recognition tools, allowing it to verbally respond to basic questions relating to stock availability or the location of items in-store.

"An Introduction to Agent-based Modeling" is a well-written and honest look at the benefits and limitations of agent-based modeling. Agent-based modeling is a computer simulation that assigns properties to agents, and the environment they interact with. Agent-based modeling demonstrates that agents acting of their own accord will collectively self-organize into predictable macro-behavior (a concept that's similar to Adam Smith's invisible hand theory). Some of this macro-behavior will alter the environment and eco-system (a concept that's termed "emergent"). The authors are honest enough to admit that agent-based modeling is not predictive (it's too determinant on the algorithms and parameters that humans assign the model), but it can be a powerful tool for education and communication.

LingPipe is a Natural Language Processing (NLP) library that is released under a dual commercial and an open-source AGPL license, and the basis for a NLP consulting company (Alias-I) that one of the authors (Breck Baldwin) founded. In fact, the preface of the book states that some of the recipes in this book come from Breck's private repository. This book is the first one devoted exclusively to LingPipe. While LingPipe provides comprehensive Javadocs and tutorials on its website, but it is fairly dense material (NLP is hard!) - the book is an easier, gentler way to understand it. One other reason LingPipe's API is so dense (even compared to other Java NLP libraries) is because it is written for performance, making heavy use of encapsulation to wrap common tasks and the visitor pattern to consume data in streaming mode. The book does a good job explaining the latter pattern in some depth, and deconstructing the code examples so the former becomes more obvious.

Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving. Python has gained widespread popularity as a computing language: It is nowadays employed for computing by practitioners in such diverse fields as for example scientific research, engineering, finance, and data analytics. One reason for the popularity of Python is its high-level and easy-to-work-with syntax, which enables the rapid development and exploratory computing that is required in modern computational work. After reading and using this book, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computational methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving.

Augmented/Virtual Reality Tools – Startups that leverage augmented or virtual reality to aid retailers in layout of stores and the design of promotional displays. InContext Solutions, which has worked with clients like Walmart, Nestle, and Kellogg's, lets brands visualize marketing concepts and test new designs on shoppers in virtual reality to gauge their efficacy before launch. Augment aims to help brands, such as General Mills, L'Oreal, and Coca-Cola, pitch their vending machines, kiosks, or merchandise displays to retailers by showing how they would look in augmented/virtual reality. Beacon-Based Analytics and Marketing – Companies that provide hardware and software to help stores track visitors. Many focus on data collection for internal analytics, such as merchandise tracking, adjusting staffing levels, monitoring promotions, etc. Euclid Analytics, for example, tracks visitors to monitor the impact of promotions on driving store visits and to better understand when people visit stores and specific aisles.

Bailey's ruminations bear rereading now that five years have passed since their first appearance. We have survived the advent of the new Millennium, and we have found once again that, after the divine afflatus, human intellect reigns supreme in the cosmos. This is not to say that Bailey's postulations have no merit. The advances of the computer age, particularly in the still-infant and arcane discipline of artificial intelligence, continue to fascinate us and to challenge us. Yet even with the burgeoning networks and the increasingly powerful integration of humanity and its machines, the surprises offered by the observations of the young and young-in-spirit still outdo those of scientific teams.

I like to watch long-term technology and business trends and watch as they shape the products and services that I get to use and to write about. As I was preparing to write today's post, three such trends came to mind: As the industry pushes forward in accord with these trends, a couple of interesting challenges have surfaced over the past decade or so. Again, here's a quick list (yes, I do think in bullet points): The GPU (Graphics Processing Unit) was born of these trends, and addresses many of the challenges! Processors have reached the upper bound on clock rates, but Moore's Law gives designers more and more transistors to work with. Those transistors can be used to add more cache and more memory to a traditional architecture, but the von Neumann Bottleneck limits the value of doing so. On the other hand, we now have large markets for specialized hardware (gaming comes to mind as one of the early drivers for GPU consumption).

Artificial intelligence (AI) is becoming ubiquitous. With advances in technology, algorithms, and sheer compute power, it is now becoming practical to utilize AI techniques in many everyday applications including transportation, healthcare, gaming, productivity, and media. Simple and natural for humans, voice communication in everyday language continues to be one of the ultimate challenges for AI. Human conversation requires the ability to understand the meaning of spoken language, relate that meaning to the context of the conversation, create a shared understanding and world view between the parties, model discourse and plan conversational moves, maintain semantic and logical coherence across turns, and to generate natural speech. Today, we are pleased to announce the Alexa Prize, a 2.5 million university competition to advance conversational AI through voice.

Through a joint alliance on artificial intelligence, five tech giants need to take a better look at the guidelines established by one of the most influential writers of modern science fiction to protect us from an apocalyptic future. Fears of what autonomous technologies are capable of is entirely understandable, even though artificial intelligence is still in early stages. IBM, -0.11% and Microsoft Corp. MSFT, -1.09% have created an ethics-focused nonprofit that says it has goals such as educating the public, protecting the privacy and security of individuals, and to create a forum for discussion of the complex issues in a future with machines may be in decision-making roles. The Partnership on AIproposed a set of eight tenets to ensure that the use of AI is "beneficial to people and society." But the corporate-sounding tenets seem to be a trumped-up, yet still watered-down, version of the three fundamental "Rules of Robotics," as written by legendary science fiction author Isaac Asimov.