AI & Machine Learning

A software robot automatically achieves the repetitive tasks much faster and with fewer errors. This brings us to the crux of Work automation – viewing it as a spectrum of work types and a spectrum of worker categories. There are many hybrids and ranges of automation in between the categories. The whole idea of humans losing jobs to automation – physical robots or robotic automation in software – needs a much holistic approach. The human factor is very much in play and repetitive jobs will be replaced with much more exciting ones. 

Deep learning troubleshooting and debugging is really hard. It’s difficult to tell if you have a bug because there are lots of possible sources for the same degradation in performance. Furthermore, the results can be sensitive to small changes in hyper-parameters and dataset makeup. To train bug-free Deep Learning models, we really need to treat building them as an iterative process. To make this process easier and catch errors as early as possible, this article suggests steps you can follow.

Maintaining a digital customer journey map seems like a lot of work, AI and machine learning can manage a lot of the heavy lifting in terms of data processing. But beyond data, you’ll want to make sure your CIO and CMO work closely together to ensure that your digital customer journey map is as cohesive as possible. No man is an island. No customer journey map holds a magic treasure. It’s a process of creating as complete of a map as you can to lead your customers where they personally want to go.

This article explains what experiment management is, and how organizing your model development process improves your workflow. Adding experiment management tools to standard software development best practices can make machine learning projects more likely to succeed. You will learn about tracking ML experiments, code version control for data science, tracking hyperparameters. You will also learn about data versioning, tracking machine learning metrics, experiment organization, working in creative iteration, and model results exploration.  

Advances in neuroscience, the study of nervous systems, provide interesting insights into how the brain works a key component for developing better AI systems. Reciprocally, the development of better AI systems can help drive neuroscience forward and further unlock the secrets of the brain. For instance, convolutional neural networks (CNN), one of the key contributors to recent advances in artificial intelligence, are largely inspired by neuroscience research on the visual cortex. Recent discoveries in neuroscience show what we’re doing right in AI, and what we’ve got wrong.

Although many advances have been made since ’80s, AI/ML is still a rapidly evolving field and human-machine interaction to support model training will continue to be a critical input for the foreseeable future. However, the nature of human-in-the-loop workflows and the expertise of the humans involved will continue to change dramatically as the annotation problems to be solved become increasingly more complex and demanding. As ML models approach the barrier of 100% accuracy, establishing ground truth intrinsically becomes more subjective, requiring increasingly higher levels of subject matter expertise and labeling precision.

Are digital humans the future of customer interaction? Chatbots are increasingly used across industries to handle customer queries and improve user experience.  As artificial intelligence technology continues to advance further, chatbots are expected to become even more sophisticated and capable of performing increasingly complex tasks. In fact, the next step in the evolution of chatbots may already be here in the form of digital humans, offering a glimpse into the future of customer interaction.

How will AI, robotics and other advanced technologies transform the very nature of work? How will labor markets evolve in our 21st century digital economy? Fundamentally redefining work is more than a nice-to-have – it is an imperative for businesses that wish to remain competitive in the 21st century.  The current trend toward more and more sophisticated automation creates the opportunity to free up capacity. It is an opportunity to shift the future of work conversation from one based in fear and adversity to one centered around hope and opportunity.

What can AI do for Manufacturing Industry? The Manufacturing industry has always been available to embrace the innovative technologies. From machinery inspection and diagnostics to production planning, AI-powered analytics enables manufacturers with the improvements in efficiency, product quality and safety of the employees. While Sensors, IoT and connectivity can fetch you operational data, advanced AI algorithms in the form of Machine Learning and Artificial Neural Networks help you to predict the next failure of a part, machine or system. 

Could 2020 be the year AI as a Service takes off? To some extent it already has. AI can lead to greater efficiencies and better customer insights for businesses in nearly every industry. In 2020, AI won’t just be nice to have, it will be a necessity. AI as a service is one of the biggest game-changers we’ll see in digital transformation. Yes, as AI as a service becomes more popular, it will also become more popular for hackers. AI as a service could definitely improve a company’s odds of experiencing success.

Machine learning is a statistical modelling technique, like data mining and business analytics, which finds and correlates patterns between inputs and outputs without necessarily capturing their cause-and-effect relationships.  Determining causal relationships requires tried-and true scientific methods, that is, empirical and measurable evidence subject to testable explanations and predictions. And, in particular, as we’re frequently reminded, correlation does not imply causation. Here are the key benefits of AI solutions based on augmenting statistical methods with domain-based models.