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Professionals within larger organizations (25,000 employees or more) are significantly more satisfied with their machine learning progress than employees in smaller companies (500 employees or less), according to Algorithmia's 2018 State of Enterprise Machine Learning study released on Tuesday. The report surveyed 523 data science and machine learning professionals to learn how companies of different sizes are using machine learning technologies, said the release. Employees from larger companies were 300% more likely to consider their machine learning efforts "sophisticated" and 80% more likely to be "satisfied" or "very satisfied" with the progression of such efforts, in comparison to smaller companies, added the release. Some 92% of respondents from larger organizations said their organization's investment in machine learning has grown by at least 25% in the past year, said the release. Larger companies have been utilizing machine learning in three main ways: Increasing customer loyalty (59%), increasing customer satisfaction (51%), and interacting with customers (48%), according to the report.

India is among other countries on the forefront of AI development. But according to a recent report, we don't seem to be doing so well at filling the jobs, created by this AI development, with early talent. In a recent report by business analytics firm Great Learning, India has more than 50,000 jobs in both data science and machine learning lying vacant. Apparently, that's because we just don't have enough talent to fill them Apparently, there are twice as many jobs available in these two professions as there are job seekers. Great Learning, in exclusive insight shared with Economic Times, says this is a clear indication that Indian professionals need to upskill.

Everything started from a conversation with a friend who one day during lunch told me "How stupid and limited Siri is. I ask her a question, and she gave me an answer. And it is so boring that I stopped using her for searching stuff. I use her only for weather and other shortcuts." "Why would you say so? Technology came a long way nowadays. You have the entire world in a metal container" I told him.

Behind most of today's artificial intelligence technologies, from self-driving cars to facial recognition and virtual assistants, lie artificial neural networks. Though based loosely on the way neurons communicate in the brain, these "deep learning" systems remain incapable of many basic functions that would be essential for primates and other organisms. However, a new study from University of Chicago neuroscientists found that adapting a well-known brain mechanism can dramatically improve the ability of artificial neural networks to learn multiple tasks and avoid the persistent AI challenge of "catastrophic forgetting." The study, published in Proceedings of the National Academy of Sciences, provides a unique example of how neuroscience research can inform new computer science strategies, and, conversely, how AI technology can help scientists better understand the human brain. When combined with previously reported methods for stabilizing synaptic connections in artificial neural networks, the new algorithm allowed single artificial neural networks to learn and perform hundreds of tasks with only minimal loss of accuracy, potentially enabling more powerful and efficient AI technologies.

Robots are coming for our jobs, and the work left over for humans is getting worse and paying less. Changes in technology and culture over the past decade have created jobs your high school guidance counselor could never imagine in their wildest dreams. Meanwhile, the safe, traditional jobs like lawyering and doctoring come with ever-increasing price tags and fewer career prospects. Unless the post-work utopia theorists are raving about comes around soon, picking your career is one of the most important choices of your life. You might as well make it one that's fulfilling and cuts a decent paycheck.

One of the discussions at eWorld recently came from Julien Nadaud, Chief Product Officer at Determine; he talked about the practical implications of AI in procurement and contract management, putting its use into real contexts. It was an interesting session, attracting a full room of delegates, in which he squarely layed out the real use cases for AI we can expect to see and how they are impacting related tasks. He's written quite a bit on that subject by the way – some of which can be found here. Procurement is becoming more'intelligent' -- this we know. AI learns continuously (with machine learning at its core) using big data coupled with historical knowledge of successful outcomes.

While most marketing managers understand that all customers have different preferences, these differences still tend to raise quite a challenge when it comes time to develop new offers. Not every product or service that your company makes will be right for every customer, nor will every customer be equally responsive to each of your company's marketing campaigns. That's why when I prepare custom training plans, I usually recommend that my clients get familiar with how they can use customer profiling and segmentation to organize their customer base into different groups. Simply put, segmentation is a way of organizing your customer base into groups. For marketing purposes, these groups are formed on the basis of people having similar product or service preferences, although segments can be constructed on any variety of other factors.

A new survey has found that 41% of consumers will shun brands that send irrelevant messages and offers. AI marketing may be the answer, however, as the survey also finds that 47% state they are happy for intelligent technology to improve the offers they receive. The survey was carried out by AI marketing outfit Emarsys, which questioned 2000 UK consumers. A mere 6% of consumers believe the product and service offers they receive are specifically relevant to them. It is not all bad; opportunity lurks too.

Digital transformation is atop the list of every marketing leader's initiatives. While there's a lot of hype around AI and machine learning, there seems to be less understanding of what it does and how it can help move the needle on business-critical goals. I'd like to demystify it and show you how you can use it to better understand your customers and how doing so can have a significant impact on your bottom line. In a time where acquiring a new customer can be 25 times more expensive than retaining an existing customer, leveraging AI to enhance your customer experience is something you'll want to include while preparing your digital transformation roadmap. Historically, marketers have created segments that group customers of a kind together and the whole group receives a similar experience.