Dec 15 (Reuters) - ChatGPT, the new chatbot that is the talk of Silicon Valley, can spit out haikus, crack jokes in Italian and may soon be the scourge of teachers everywhere facing fake essays generated by the AI-powered technology. But a question it can't fully answer is this: How will OpenAI make money? The research organization, co-founded by Elon Musk and investor Sam Altman and backed by $1 billion in funding from Microsoft Corp (MSFT.O), is expecting its business to surge. Three sources briefed on OpenAI's recent pitch to investors said the organization expects $200 million in revenue next year and $1 billion by 2024. The forecast, first reported by Reuters, represents how some in Silicon Valley are betting the underlying technology will go far beyond splashy and sometimes flawed public demos.

When we include the unprecedented computing power offered by the cloud, it's clear we are living in an exciting era for building applications. When IBM Watson defeated the two Jeopardy champions back in 2011, it opened a new era in the practical application of Artificial Intelligence technology and contributed to the growing research and interest in this field. IBM Watson has evolved from being a game show winning question & answering computer system to a set of enterprise-grade artificial intelligence (AI) application program interfaces (API) available on IBM Cloud. These Watson APIs can ingest, understand & analyze all forms of data, allow for natural forms of interactions with people, learn, reason – all at a scale that allows for business processes and applications to be reimagined. This course is intended for business and technical users who want to learn more about the cognitive capabilities of IBM Watson Discovery service.

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Data Science has gained a lot of popularity in the last few years. This field's primary focus is to convert meaningful data into marketing and business strategies which helps a company grow. The data is stored and researched to get in a logical solution. Previously only the top IT companies were involved in this field but today businesses from various sector and fields such as e-commerce, health care, finance, and others are using data analytics. There are various tools available for data analytics such as Hadoop, R programming, SAS, SQL and many more. However the most popular and easy to use tools for data analytics is Python.

A recent survey from Figure Eight – an Appen company – shows that AI (Artificial Intelligence) is rapidly becoming a strategic imperative. But unfortunately, there are major bottlenecks, such as the divides between line-of-business owners and technical practitioners as well as the complexities of managing data. But there is something that should help solve the problems: low-code. As the name implies, this involves creating applications with drag-and-drop and integrations. The result is that development is much quicker and effective (here's a post I wrote for Forbes.com

It used to be that I could easily ignore the news coming from Redmond. Sure, Microsoft was always important, but I was never a .NET or Windows developer, so what they were saying rarely applied to me. I might be impressed by the quality of their tooling, like Visual Studio, but I didn't write C#, so it didn't really matter. Microsoft has an outsize role in most every developer's career. Even if you don't directly use their products or services, the direction they take has an enormous impact on us.

Information Access: Building applications to improve access to information in massive text collections, such as the web, newswires and the scientific literature. Subtopics include: information extraction, text mining and semantic annotation, question answering, summarization. Includes platforms for developing and deploying real world language processing applications, most notably GATE, the General Architecture for Text Engineering. Machine Translation: Building applications to translate automatically between human languages, allowing access to the vast amount of information written in foreign languages and easier communication between speakers of different languages. Human-Computer Dialogue Systems: Building systems to allow spoken language interaction with computers or embodied conversational agents, with applications in areas such as keyboard-free access to information, games and entertainment, articifial companions.

You might go to a cutting-edge machine learning research conference like NIPS hoping to find some mathematical insight that will help you take your deep learning system's performance to the next level. Unfortunately, as Andrew Ng reiterated to a live crowd of 1,000 attendees this past Monday, there is no secret AI equation that will let you escape your machine learning woes. All you need is some rigor, and much of what Ng covered is his remarkable NIPS 2016 presentation titled "The Nuts and Bolts of Building Applications using Deep Learning" is not rocket science. Andrew Ng delivers a powerful message at NIPS 2016. Andrew Ng's lecture at NIPS 2016 in Barcelona was phenomenal -- truly one of the best presentations I have seen in a long time.

The Slackbot is getting a "cognitive" makeover. IBM Watson, Big Blue's big gamble on machine learning, is preparing to perform brain surgery. IBM IBM has partnered with Slack, the fast-growing workplace messaging app, to power the next iteration of Slackbot, Slack's flagship virtual assistant, the companies said Wednesday. Slack said it plans to adopt "Watson Conversation," a month-old tool that helps bots interact through natural language, for its own assistant. In addition, IBM and Slack are planning to develop new artificially intelligent chat features, developer tools, and a brand new bot aimed at assisting I.T. teams and administrators. Developers building applications on top of Slack will be able to draw on various Watson-powered tools, including sentiment analysis, which parses speech to infer emotion, and speech plug-ins, which translate text to spoken language, the company said.