AI & Machine Learning

With the advent of AI, marketers are moving towards mass personalization and mass hyper-personalization. But the key question is – is it relevant or creepy? Are you using AI effectively and sensibly, so as to not freak out your customers but delight them instead? Unless you discover the right signals, you will invariably be looking at the wrong insights and interpretations, leading to bad intelligence. A significant difference, between coming across as creepy and relevant, depends on how to build a conversation using the insights you have gathered. 

The insurance industry collects and generates a large volume of data on a daily basis, including a customer’s health records, sensor data from vehicles, confidential legal papers, to name a few. The data, if analyzed thoroughly, gives actionable insights that the insurance industry can use to improve its services. Deep learning comes with neural networks that are capable of analyzing swarms of data and learning from it. Deep learning in insurance not only enhances customer experience but also helps the industry detect fraudulent activities.

Spurred by the capabilities of deep learning, and how it has so far defied the norms set by traditional software, many organizations and visionaries are thinking once again that strong AI is on the horizon and want to catch it before others do.  But while all this talent focuses on finding a way to create strong AI that can compete with the human brain, we’re missing out on plenty of the opportunities and failing to address the threats that current weak AI technology presents.

A great many traditional IT engineers are enthusiastic about learning/contributing to the exciting field of data science and machine learning/artificial intelligence. However it will be incomplete in your preparation for having solid grasp over machine learning or data science techniques without having a refresher in some essential mathematics. Then the question is: What are the essential topics/sub topics of mathematics that an average IT engineer must study/refresh if (s) he wants to enter into the field of business analytics/data science/data mining? How much mathematics does an IT engineer need to learn to get into machine learning?

Machine learning can help streamline the delivery of healthcare services, and one of the biggest issues that need to be addressed is the future role of machine learning in healthcare litigation. The real benefit of machine learning is that it can process massive data sets to help healthcare professionals make better decisions to improve patient treatments, yield more accurate diagnoses and minimize costs without compromising the quality of care. Healthcare litigators have realized that big data is changing their profession in countless ways. They are exploring new ways to use machine learning algorithms to find the most lucrative cases and develop winning strategies. 

Analytic Modules are pre-built engines that can be assembled to create specific business and operational applications.  They produce pre-defined analytic results or outcomes, while providing a layer of abstract that enables the orchestration and optimization of the underlying machine learning and deep learning frameworks. One example of an IoT analytic modules would be Anomaly Detection.  Anomaly detection is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. A number of different machine learning techniques can be used to help flag and assess the severity of detected anomalies.

One of the domains where the General Data Protection Regulation (GDPR)will leave its mark prominently is the artificial intelligence industry. Data is the bread and butter of contemporary AI, and under previously lax regulations, tech companies had been helping themselves to users’ data without fearing the consequences. That will change on May 25, when GDPR comes into effect. GDPR requires all companies that collect and handle user data in the European Union to be more transparent about their practices and more responsible for the security and privacy of their users. 

Behind the revolution in digital assistants and other conversational interfaces are natural language processing and generation (NLP/NLG), two branches of machine learning that involve converting human language to computer commands and vice versa. NLP and NLG have removed many of the barriers between humans and computers, not only enabling them to understand and interact with each other, but also creating new opportunities to augment human intelligence and accomplish tasks that were impossible before. Maybe NLP and NLG will remain focused on fulfilling more and more utilitarian use cases.

Machine learning is known for its difficulties with interpretability, or rather its absence. This is an issue if your users have to work with the numeric output, like in the systems used in sales, trading or marketing. If the user’s interpretation of the ML output is wrong the actual metrics won’t matter and you end up with the bad user experience. The problem is even bigger if you try switching users from an old transparent algorithm to ML. Here I outline the recipes for overcoming the user’s push-back once you start switching your system to ML.

The views on AI software development are polarized and controversial. Still, many experts see AI as an opportunity rather than a threat. AI technology may act as a method of augmenting human workforce and enabling us to work in newer and smarter environments rather than disrupting every single aspect of our lives. But like with any powerful technology, we need to use it prudently. Here are the most common illusions and myths about AI software development and shed some light on the possibilities of this disruptive technology.

Experienced teams know when to back up seeing a piling debt, but technical debt in machine learning piles extremely fast. You can create months worth of debt in a matter of one working day and even the most experienced teams can miss a moment when the debt is so huge that it sets them back for half a year, which is often enough to kill a fast-pacing project. You end up with the project where the metrics randomly jump up or down, do not reflect the actual quality, and you are not able to improve them.