In early 2011, Ken Jennings looked like humanity's last hope. Watson, an artificial intelligence created by the tech giant IBM, had picked off lesser Jeopardy players before the show's all-time champ entered a three-day exhibition match. At the end of the first game, Watson--a machine the size of 10 refrigerators--had Jennings on the ropes, leading $35,734 to $4,800. On day three, Watson finished the job. "I for one welcome our new computer overlords," Jennings wrote on his video screen during Final Jeopardy. Watson was better than any previous AI at addressing a problem that had long stumped researchers: How do you get a computer to precisely understand a clue posed in idiomatic English and then spit out the correct answer (or, as in Jeopardy, the right question)?

Human thinking is beyond imagination. Can a computer develop such ability to think and reason without human intervention? This is something programming experts at IBM Watson are trying to achieve. Their goal is to simulate human thought process in a computerized model. The result is cognitive computing – a combination of cognitive science and computer science. Cognitive computing models provide a realistic roadmap to achieve artificial intelligence.

ChatGPT3 became the newest internet sensation last year when it allows users to generate text and answer complex questions in a manner that seems almost human. But, beyond the prowess of ChatGPT3, the underlying impact of the technology -- generative AI -- on business is only just coming into focus. ChatGPT3, together with its image-generating cousin Dall-E, has the potential to revolutionize the way content is created, from blogs to white papers, student essays to business correspondence. It provides access to expert-level syntax and grammar to anyone who uses it. But this also raises some important ethical questions.

Beyond common features like ML and automation, CRM products can vary dramatically as each vendor takes AI in its own direction. The following AI-powered CRM platforms -- including Salesforce Einstein, IMB Watson and Azure Cognitive Services -- have their own strengths and weaknesses. Salesforce Einstein is the vendor's AI that powers many features in the Salesforce Customer Success Platform. Einstein's weaknesses include modest visualization features and limited or unproven utility beyond the sales and marketing domains. IBM Watson is an AI system that organizations can apply in various use cases, such as advertising, customer service, financial operations and sales.

A decade of big data investments, combined with cloud scalability, the rise of more cost effective processing and the introduction of advanced tooling, has catapulted machine intelligence to the forefront of technology investments. No matter what job you have, your operation will be AI powered within five years and machines may be doing your job in the future. Artificial intelligence is being infused into applications, infrastructure, equipment and virtually every aspect of our lives. AI is proving to be extremely helpful at controlling vehicles, speeding medical diagnoses, processing language, advancing science and generally raising the stakes on what it means to apply technology for business advantage. But business value realization has been a challenge for most organizations because of a lack of skills, complexity of programming models, immature technology integration, sizable up front investments, ethical concerns and lack of business alignment. Mastering AI technology and a focus on features will not be a requirement for success in our view. Rather, figuring out how and where to apply AI to your business will be the crucial gate.

Machine learning tools are getting hyper-attention due to their wide-scale application across industries for high-velocity and accurate predictive analytics. If you think it's getting hard, don't stress; this article will clear all your doubts to know more about machine learning and its applications. Machine learning (ML) facilitates software applications to forecast behaviors with better accuracy. The ML state-of-the-art algorithms use existing data (also called historical data) to predict future outcome values. According to the SEMrush Report, approximately 1 billion machine learning and AI experts and data analysts will be needed by 2025.

I have recently updated my extensive analysis of the popularity of data science software. This update covers perhaps the most important section, the one that measures popularity based on the number of job advertisements. I repeat it here as a blog post, so you don't have to read the entire article. One of the best ways to measure the popularity or market share of software for data science is to count the number of job advertisements that highlight knowledge of each as a requirement. Job ads are rich in information and are backed by money, so they are perhaps the best measure of how popular each software is now.

With the ability to revolutionize everything from self-driving cars to robotic surgeons, artificial intelligence is on the cutting edge of tech innovation. Two of the most widely recognized AI services are Microsoft's Azure Machine Learning and IBM's Watson. Both boast impressive functionality, but which one should you choose for your business? Azure Machine Learning is a cloud-based service that allows data scientists or developers to train, build and deploy ML models. It has a rich set of tools that makes it easy to create predictive analytics solutions. This service can be used to build predictive models using a variety of ML algorithms, including regression, classification and clustering.

Opinion It started in Jeopardy and ended in loss. IBM's flagship AI Watson Health has been sold to venture capitalists for an undisclosed sum thought to be around a billion dollars, or a quarter of what the division cost IBM in acquisitions alone since it was spun off in 2015. Not the first nor the last massively expensive tech biz cock-up, but isn't AI supposed to be the future? Isn't IBM supposed to be good at this? It all started so well.

Audience for this article: I designed a generic chatbot framework and discussed in this article to cover a wide range of audience. Anyone who has a basic knowledge of Python, Jupyter notebooks and can perform pip installations should be able to complete this series and see the results. The bot framework is modularized which opens up an array of opportunities for the readers to design and implement their own features. Integrations can be done easily in the framework. Also, the probability for failure is minimal since it is designed to be plug and play.