Rapid advancements in the field of artificial intelligence (AI) are uniquely poised to transform entire occupations and industries, changing the way work will be done in the future. It is imperative to understand the extent and nature of the changes so that we can prepare today for the jobs of tomorrow. New empirical work from the MIT-IBM Watson AI Lab uncovers how jobs will transform as AI and new technologies continue to scale across business and industries. We created a novel dataset using machine learning techniques on 170 million U.S. job postings. The dataset and research, The Future of Work: How New Technologies Are Transforming Tasks, allow us to extract key insights into how AI is shaping the future of work.

IBM Watson is considered to be the first-ever commercialized cognitive computing platform, designed specifically for underpinning the development of various enterprise solutions. IBM Watson services continue to tap immense opportunity in the rapidly evolving cognitive computing field, which has been reshaping the nature of business operations, thereby determining their growth. Fact.MR's recent study projects the IBM Watson services market to record a spectacular rise in the period of forecast (2018-2028). Over US$ 20,000 Mn worth of IBM Watson services are estimated to be sold globally by 2028-end. Although cognitive computing is yet at its nascent phase, the technology is expected to have a significant influence on transformation of various businesses and industrial sectors.

Multi-channel is the new norm – consumers are not completely abandoning brick-and-mortar stores. Instead, they expect seamless shopping experiences across online, mobile and offline stores. They might first browse and research online, then purchase or pick-up in-store--or the other way around. Successful retailers who can gain customer loyalty are those who can deliver a superior seamless experience across all channels. Data is the new gold – The additional touchpoints mean retailers have greater opportunity and more data to identify their customers and discern their preferences. However, without a proper data and analytics infrastructure, many retailers struggle to mine and analyze huge volumes of data generated daily to gain valuable insights that can help them innovate.

Businesses today are increasingly certain that AI will be a driving force in the evolution of their industries over the next few years. To successfully infuse AI into your product or solution, there are many factors that challenge its widespread adoption in the business–and to achieving your expected outcomes. Building trust – Organizations and businesses tend to be skeptical about AI because of its "black box" nature, resulting in many promising models not going into production. Algorithm bias – Another inherent problem with AI systems is they're only as good–or as bad–as the data they're trained on. If the input data is filled with racial, gender, communal or ethnic biases, your model's accuracy is going to eventually drift away.

There are many reasons why you would want to leverage Watson Assistant to make your Einstein Bot "better". In a previous blog, I spoke to just some of the key reasons why you would need to do so. I will provide additional detail here but first, let's look at how you integrate Watson into your Einstein Bot. The obvious table stakes, you need a Watson Assistant service to integrate with Bots. If you don't already have one, you can get a free IBM Cloud account to deploy a Watson Assistant service, which you can do in about a minute, also for free.

At the front lines of business communications lies the customer service team, a vital link between customers and your organization. With rising customer expectations and multiple communications channels available, connecting with customers in their preferred method is essential to a positive experience. Customer experience (CX) improvements are driven by new technology, and each customer interaction impacts the user's relationship with your organization. AI is one of these technologies that can improve your customer experience and contact center. For example, AI can connect the dots between the maze of data in your contact center and change the way your teams interact with your customers.

Apple introduced CoreML in WWDC 2017, and it is a great deal. CoreML is a machine learning framework used in many Apple products, like Siri, Camera, Keyboard Dictation, etc. The cool stuff about CoreML is that it can use a pre-trained model to work offline. Apple has provided lots of pre-trained models like MobileNet, SqueezeNet, Inception v3, VGG16 to help us with image recognition tasks, especially detecting dominant objects in a scene. The job of CoreML is simply predicting data based on the models.

Data science is a required practice for organizations accelerating their journeys to AI. Businesses are keen on hiring the right talent, acquiring the right tools and evolving the discipline. Solving the lack of data scientists' problems requires investment in our employees in terms of time and training. We can't expect these people to just keep on learning for a year before they can be productive. We need to reach a stage where people know enough to start contributing immediately while continuing to improve their skills. As far as the second problem is concerned, taking too much time getting to a usable and tuned model, we need tools to help us optimize our data scientists' productivity.