Using its private network of billions of time-sensitive intent interactions, 6sense uncovers net-new prospects at every stage of the funnel and determines which existing prospects are in market to buy.

Now machine learning is seen as the new way forward in software development. It is seen as potentially able to take the programming out of programming. Google research engineer Peter Warden said, "In 10 years, I predict most software jobs won't involve programming," in a recent article in TopBots. Waterfall software development, also called the Systems Development Life Cycle (SDLC), entailed a programmer using a language such as Python or C to write code to deliver on the application requirements. These came from a requirements definition stage, often involving a software business analyst sitting with a business executive.

In 1971, Terry Winograd wrote the SHRDLU program while completing his PhD at MIT. SHRDLU features a world of toy blocks where the computer translates human commands into physical actions, such as "move the red pyramid next to the blue cube." To succeed in such tasks, the computer must build up semantic knowledge iteratively, a process Winograd discovered was brittle and limited. The rise of chatbots and voice activated technologies has renewed fervor in natural language processing (NLP) and natural language understanding (NLU) techniques that can produce satisfying human-computer dialogs. Unfortunately, academic breakthroughs have not yet translated to improved user experiences, with Gizmodo writer Darren Orf declaring Messenger chatbots "frustrating and useless" and Facebook admitting a 70% failure rate for their highly anticipated conversational assistant M. Nevertheless, researchers forge ahead with new plans of attack, occasionally revisiting the same tactics and principles Winograd tried in the 70s. OpenAI recently leveraged reinforcement learning to teach to agents to design their own language by "dropping them into a set of simple worlds, giving them the ability to communicate, and then giving them goals that can be best achieved by communicating with other agents."

Video recording of talk given at ODSC West on November 3rd, 2017 "State of Conversational AI" gives a technical overview & review of current state-of-the-art deep learning & NLP tactics for chatbots and conversational interfaces as well as product UX / design tips for crafting customer experiences around current limitations in conversational AI. Talk covers recent research papers, metrics and lessons from live products, and also a review of conversational AI platforms available for enterprise usage. The target audience is product leaders, technical executives, and designers and developers involved in the crafting of customer-facing conversational experiences. Mariya is the CTO and Head of Research & Design at TOPBOTS, a strategy & research firm focused on applied artificial intelligence and machine learning. As an "AI Designer", she combines UX and AI to create intelligent tools and lovable products.

We don't have to wait for AI to gain sentience and go rogue because the probability of intelligent automation being used by bad people for bad purposes is one…hundred…percent. Expect chaos when con artists conspire with convoluted neural networks. Assessing threats and preemptively developing defense mechanisms is critical for the good guys to prepare for, prevent, and mitigate potentially existential threats. That's the core message of a comprehensive and rather alarming report prepared by some of the brightest (ahem, human) minds on artificial intelligence. Aptly entitled "The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation," the 100-odd page policy recommendation paper was authored by luminaries from Future of Humanity Institute, OpenAI, Centre for the Study of Existential Risk, and leading universities in the U.S. and U.K. In the report, the authors described the fast-evolving threat landscape, key areas of security risk, and high-level recommendations for actions we can take today.

The market for AI is so overhyped, virtually anybody looking for a fast buck can re-package an old abacus and sell it as a "machine learning" platform. Misinformed people buy the idea and the mounting frustration makes it extremely difficult for legitimate AI companies to get their message above the din. To successfully stand out in a crowded market, creative marketers have found it critical to create unconventional educational content and enlist the support of credible B2B influencers in their space. In the ever-shifting AI solutions ecosystem, even expert-led and well-thought out marketing campaigns can -- and often -- fail. To reduce costs and save time, marketers of AI products need to learn at the onset, or as early as possible, exactly which specific method doesn't deliver the results for their solutions.

Bigoted AI systems rightly deserve the universal flak they get. But there's a problem: we might still be screwed even if AI is neutral. Unless artificial intelligence has the "right" biases, AI and people won't coexist very well. Unless purposely wired to be ethical and people-centric, AI can be commandeered for malicious purposes or inadvertently cause serious socio-economic problems, not least of which is the precipitous loss of jobs. With machines already beating humans at manual tasks, games, back-office work, and even lawyering, who knows whether there'll be any job left in the future for the vast majority of humans to build their careers on.

AI is transforming all business functions, and software development is no exception. Not only can machine learning techniques be used to accelerate the traditional software development lifecycle (SDLC), they present a completely new paradigm for inventing technology. Traditionally, developing a computer programs requires you to specify in advance exactly what you want the system to do and then hand engineer all of the features of your technology. Encoding many tasks in an explicit way is possible, as computers before the advent of AI were still quite powerful. There are many tasks and decisions, however, that are far too complex to teach to computers in a rigid, rule-based way.

Artificial Intelligence (AI) is having a transformative effect on the business world and the $600 billion global legal services market is not immune. As AI automates basic processes, in the legal profession it promises to allow lawyers devote their time to more valuable, cost-effective, and strategic work. Consultants at McKinsey & Company estimate that 22% of a lawyer's job and 35% of a paralegal's job can be automated. However, the common perception among lawyers remains that machines cannot yet match the legal intellect of human lawyers in daily fundamentals of the profession. This assumption was tested in the first of its kind "AlphaGo"-style Study in the legal profession.

Adelyn, Marlene, and I had the honor of being invited to speak at CES 2018 about our book, Applied Artificial Intelligence: A Handbook For Business Leaders. Other authors featured in our cohort this year included John Grisham and Stephen Wolfram. In the past, Deepak Chopra and Alexis Ohanian have spoken on stage about their work. We were extremely lucky to have Cindy Stevens, Senior Director of Publications for the Consumer Technology Association (CTA), be our interviewer. She and the rest of the CTA team made what could have been a high-pressure situation feel streamlined and relaxed. We're excited to present our CES 2018 book interview along with the transcript of the event below: CS (Cindy Stevens): Welcome to Gary's book club at CES 2018. Today, we're going to talk about artificial intelligence and how it is being integrated into product across the CES show floor. I know you have already seen a lot of that already. AI promises to improve our lives tremendously but many of you may be wondering what AI means for your business.