AI & Machine Learning

No leader should deploy intelligent and autonomous applications without a thorough understanding of how the system works, from the variables used for analysis to the key outcomes tracked for success. Without the right data and the proof needed to recommend and execute actions, the system is actionable. Leaders cannot, and will not, move to a model of intelligent applications without the trust and transparency provided by the education and monitoring piece. But when the five components of AI are integrated and made easily available to leaders, AI can begin to guide strategy in all types of organizations.

This post gives an introduction to the field of Artificial Intelligence and a better understanding of how AI works and what it can really do. You will learn about the common misconception about AI and what Machine Learning and Data really is, and familiarise with the most common terms of the field: Data Science, Deep Learning, AI and Machine Learning. You can also learn where you can get data, how to approach data acquisition and that having a lot of data does not necessarily mean that you can do AI with it.

Neuromorphic computing is not new. It was first proposed in the 1980s. But recent developments in the artificial intelligence industry have renewed interest in neuromorphic computers. The growing popularity of deep learning and neural networks has spurred a race to develop AI hardware specialized for neural network computations. Among the handful of trends that have emerged in the past few years is neuromorphic computing, which has shown promise because of its similarities to biological and artificial neural networks.

For nearly a decade, healthcare professionals have been introduced to “quick-fix” automation solutions that have failed to work cohesively with existing systems. As a result, these implementations have largely failed – causing distrust amongst employees about the value of new technologies. However, RPA is different in that it is quick to implement, seamlessly integrates with existing systems and delivers near-immediate value. Here, we debunk common myths about RPA in healthcare to explain how technology can free up time and resources spent on administrative tasks.

Understanding a phenomenon is the first step toward engineering it, so if we have an explanation of consciousness we can hope to succeed in building the same functionality into our AIs. In any case, it is very unlikely that artificial intelligence with no ability to explain its reasoning with human concepts will be socially acceptable. Equip it with human characteristics such as consciousness would probably be the only way for us to trust it and solve the black box problem, that is to say artificial consciousness out of necessity.

We’ve already seen some of the threats manifest themselves in various ways, including biased algorithms, AI-based forgery and the spread of fake news during important events such as elections. The past few years have seen the development of a growing discussion around building trust in artificial intelligence and creating safeguards that prevent abuse and malicious behavior of AI models. The various efforts are focused in three fields of fairness, explainability and robustness.  It’s important to create robust AI models and to evaluate the resilience of artificial intelligence algorithms against abuse and erratic behavior. 

As AI gets better and more sophisticated, it also enables cybercriminals to use deep learning and AI to breach security systems just as cybersecurity experts use the same technology tools to detect suspicious online behavior. Deepfakes, using AI to superimpose one person’s face or voice over another in a video, for example, and other advanced AI-based methods will probably play a larger role in social media cybercrime and social engineering. It sounds scary, and it’s not science fiction.

Today, when customers seek instant responses to their queries, chatbots are a popular solution. They are already making their way to messaging mediums. The real challenge, though, lies in building a chatbot that is engaging, interactive and is able to provide real value to users. With easy-to-use chatbot platforms, making a chatbot is now quick and hassle-free. And with unprecedented technological advancements in artificial intelligence and machine learning, the future of chatbots looks bright.

You could say there is too much AI hype. It is not a contentious thing to say, it just is. Maybe it would help if we re-defined it. Instead is saying AI means artificial intelligence, maybe we should return to an earlier definition, algorithmic intelligence, instead. Artificial intelligence is the application of algorithmic computation to large data sets. To see through the hype, just remember that AI could just as easily mean algorithmic intelligence.

If robots are truly taking over, then why are having trouble finding enough humans to do work that needs being done? The truth is that automation doesn’t replace jobs, it replaces tasks and when tasks become automated, they largely become commoditized. So while there are significant causes for concern about automation, such as increasing returns to capital amid decreasing returns to labor, the real danger isn’t with automation itself, but what we choose to do with it.

In the past few years, telepresence robots have been around, but have not gained widespread traction in the enterprise.  Yet, increasingly we are seeing classes of robots designed to interact with people in a distributed physical space. Moving into 2019, we will see an expansion of these office and personal robots meant to help us collaborate and coordinate action with others. As the year comes to an end, here are some predictions on digital workplace, specifically, the rise of the cobot – a robot interacting and functioning with people.