In today's digital world, it is more important than ever to ensure that your data is protected especially with the rise of machine learning also known as artificial intelligence (AI). Machine learning is a popular technology topic as it's becoming a part of our daily lives and can potentially have powerful implications for good and evil. In case you are not familiar with the terms machine learning or artificial intelligence, it is having the ability to train a computer to do something and learn over time so down the road it can infer what to do when faced with a basic task. Just a few examples of common consumer facing artificial intelligence machines are Apple's Siri, Google Assistant and Amazon's Alexa. With these machines learning our habits and likes/dislikes overtime, we are able to make our daily lives easier whether it's getting an answer to a question, directions to a local store or restaurant recommendations.

Artificial intelligence, for better or worse, is playing an increasingly important role in cybersecurity. Organizations can use modern AI-based tools to detect threats better and protect their systems and data assets. However, cybercriminals can also use this technology to launch more sophisticated attacks. Rising cyberattacks are contributing to the growth of the AI-based security products market. According to his July 2022 Acumen Research and Consulting report, the global market will be worth US$14.9 billion in 2021 and is projected to reach US$133.8 billion by 2030.

Machine translation has been around for many years. However, it wasn't until Google, Microsoft and others began developing machine translation that it grew into a serious competitive alternative to human translation. As a result, machine translation has made more progress in the last 10 years than the previous 50 years. Today, machine translation is used to produce billions of words daily and is fast closing in on human translation quality. At the heart of the improvement in machine translation quality is artificial intelligence.

The need for the adoption of AI/ML in Life science companies has increased as the Industry evolves and moves towards high tech cures and precision medicine that is based on specific substances and production methods. The entire value chain is broken down and executed by specialist providers. The Pharma company creates the formula,production is done by specialized contract manufacturers, packaged by specialized companies, medical distributors storing the drugs at specialized conditions before reaching the pharmacies and patients. Managing this complex process for thousands of therapies makes it imperative for Lifesciences companies to start their AI/ML journey at the earliest. There are varied use cases being adopted by companies across the value streams starting from Drug discovery, Clinical trials, Manufacturing, Supply chain, Commercial to Post market surveillance.

As technical innovations in Autotech industry took a fast pace over the last few decades, much has been done to add to the delight of the car lovers. From old wooden basic steering wheel to modern Tesla self-driving features, advanced technology has made a shift to next generation Autotech mobility systems –a possibility. While 90% of car accidents are attributed to human mistakes, introduction of Artificial Intelligence in Autotech industry will significantly help in reducing accidents. Disruptive innovations will transform the Autotech industry completely. Soon petrol-fuelled cars will become a history. Many of such trends are just around the corner and we will soon be able to see latest Autotech trends entering the showrooms this year.

Every time you read a trade journal, an article on LinkedIn or attend a conference you can bet there'll be something about AI and Big Data (it's always capital B and capital D too). It's also probable that many businesses will be able to get along fine without either. However, anyone wanting to profit from these innovations will be finding out exactly how they can assist them. On the one hand, AI will undoubtedly help in processes, transactions and compliance. Machine learning will reduce time, cost and complexity from many arduous jobs within companies, businesses and firms.

Despite the recent successes in the use of machine learning (ML) to perform tasks more accurately than humans such as in cancer detection and hand-writing recognition, moving beyond a demo to roll out a live commercial product requires much more than a fancy algorithm. There's no doubt that the technology will re-define most industries, but it's worth keeping in mind that we are just at the beginning of a multi-decade cycle and so entrepreneurs should be cognisant of this when implementing ML in a commercial environment with clients. What follows are some lessons learnt from being in the trenches. It's worth clarifying that most problems in ML follow a similar pattern, loosely called "predictive analytics": Building these models requires huge datasets of labeled historical records. For example: loan applications with tags stating whether a loan repayment event occurred in order to make a prediction whether an applicant will repay a loan. The dataset requirement may not sound problematic (even assuming the requisite datasets are available), but the reality is that these datasets are often residing in different areas of the business, in different formats with different labels and with different decision-makers for each dataset.