AI & Machine Learning

Deep learning, the spearhead of artificial intelligence, is perhaps one of the most exciting technologies of the decade. There’s no doubt that machine learning and deep learning are super-efficient for many tasks. Deep learning is often compared to the mechanisms that underlie the human mind. However, they’re not a silver bullet that will solve all problems and override all previous technologies. Beyond the hype surrounding deep learning, in many cases, its distinct limits and challenges prevent it from competing with the mind of a human child.

Companies are leveraging chatbots for marketing activities, to generate leads, to entertain their target audience, and for customer service. Consumers are also aware of the benefits of interacting with smart bots. So if you’re interested in leveraging chatbots to better engage with your customers, what’s the best way to approach it? There are a few ways to incorporate chatbots into your enterprise offering. If you’re serious about using chatbots to drive enterprise value, you just can’t take the uncertain route of a DIY bot platform. There’s too much risk and not enough return to justify such an investment.

This an article about Neural Networks dedicated to programmers who want to understand basic math behind the code and non-programmers who want to know how to turn math into code. In the last years applications of Deep Learning made huge advancements in many domains arousing astonishment in people that didn’t expect the technology and world to change so fast. Deep Learning is currently excelling in tasks like image recognition, Autonomous Vehicles, games like Chess, and also computer games, Language Translation, and speech recognition.

Artificial intelligence is an incredibly complicated concept for application testing. There aren’t that many products that offer real AI/machine learning functionality for app QA. Your best bet is to find a QA team that has in-house machine learning solutions or uses one of the tools that we mentioned and their alternatives. This way, your app testing needs will get the maximum coverage that they deserve.  It’s also important to remember that traditional QA automation still works. You don’t have to jump on the AI bandwagon just because everyone is using it in their marketing nowadays. 

Cognitive Brain Cloud Computing coupled with Blockchain Based security models is having a moment using latest technical jargon in IT world. Artificial neural network algorithms like deep learning, which are very loosely based on the way the human brain operates, now allow digital computers to perform such extraordinary feats as translating the language, hunting for subtle patterns in huge amounts of data, and beating the best human players at Go. But even as engineers continue to push this mighty computing strategy, the energy efficiency of digital computing is fast approaching its limits. 

Machines replaced operators, manual labor, now they are coming after many more professions. Technology aids at removing obstacles and difficulties from our lives, and so we must face these challenges. Every profession will have to feel these pressures or else it will mean we are not improving, innovating or trying to transform. Since we have no choice we cannot slow down. In fact, we have to speed up and think what else machines can do, because to be successful we have to be faster, effective and efficient than earlier. We have to understand, unskill, unlearn, unsettle, innovate traditional ways of thinking.

Ready to learn Machine Learning? Browse courses like Machine Learning Foundations: Supervised Learning developed by industry thought leaders and Experfy in Harvard Innovation Lab. Machine learning(ML) and AI are hot topics these days. So, I find a lot of product managers and would-be product managers come up to me and ask how they can become better ML

Ready to learn Machine Learning? Browse courses like Machine Learning Foundations: Supervised Learning developed by industry thought leaders and Experfy in Harvard Innovation Lab. This is a continuation of the three part series on machine learning for product managers. The first note focused on what problems are best suited for application of machine learning techniques. This note would delve

As big data gradually comes into play in the world of business, machine learning has become one of the most important tools for companies to solve various kinds of problems. Firms across various industries are all trying to incorporate this rising technique into their business and get a competitive edge in understanding their consumers better. Here’s taking a look at the basics of machine learning and how your organization can benefit from it.

Machine learning is great, but its really easy to quickly become disenfranchised by it if you’re not going about it the right way. But your business and understanding of machine learning needs to take occasional false positives and negatives into account.

Ready to learn Machine Learning? Browse courses like Machine Learning Foundations: Supervised Learning developed by industry thought leaders and Experfy in Harvard Innovation Lab. There is a war a-brewin’, but this war will be fought with wits and not brute strength. Ever since Russian President Vladimir Putin’s declaration that “the nation that leads in AI (Artificial Intelligence)