Compared to a physical factory, software production is cheap to change over and doesn't require capital investment to be ripped out and replaced. And turning developers into high velocity code assembly lines wastes a huge opportunity to cultivate highly trained, creative, innovative people. An alternative is to approach AI development similar to the way my dad approached woodworking in his garage. A developer is not an executor of code but a creator. Tools exist to affect the creator's vision, and the vision adapts based on the productive experience.

The use of artificial intelligence (AI) technologies has spread across the insurance value chain. In product development, it enables insurers to create more profitable and effective products based on insights from past claims and product uptake in the market. In underwriting, it creates a better understanding of risk for new and underserved markets. Even so, the potential of AI has yet to be fully realized. For now, AI's role in the industry is largely limited to optimizing existing business processes rather than developing new and disruptive business models.

Tech companies and the auto industry are working hard in tandem to make autonomous driving a reality by the early 2020s. Driverless cars with various levels of human participation will roll out in stages over the next few years, with fully-autonomous SAE Level 5 driving on the scene by 2030. Today, most automotive manufacturers have achieved Level 2 assisted driving where the car can manage simple scenarios, like active lane centering and parking assistance, itself. Fewer manufacturers provide Level 3 autonomous driving where the car can autonomously navigate a traffic jam or roadways to a destination. For both levels, human drivers can take the wheel if they choose.

The current wave of artificial intelligence (AI) works by using computer models to simulate intelligent behavior. Machine-learning algorithms are good at learning new behaviors, but bad at identifying when those behaviors are harmful or don't make sense. Companies deploying AI will need a workforce trained to ensure that the technology remains both useful and safe. AI at DXC: Artificial Intelligence is any program that does something that we would think of as intelligent in humans. AI is often based on machine learning and can produce unexpected results.

While many companies are interested in applying artificial intelligence (AI) and machine learning (ML) to help their businesses transform and innovate, few have an appetite for a lengthy project or a large initial investment. But when companies can see meaningful value in six to eight weeks, they are more likely to expand their usage of AI and ML into new initiatives. To demonstrate the value proposition for AI and ML, we recommend concentrating on unlocking insights from vast amounts of unstructured data to help augment human intelligence, create process efficiencies and lower operational costs. We have also found that the best results tend to come from a business-led/IT supported model. Four key questions are central to scoping out an initial minimal viable product (MVP) for a valid use case.

Artificial intelligence (AI), machine learning and deep learning have become entrenched in the professional world. AI-style capabilities are being embraced and developed globally (over 26 countries/regions have or are working on a national AI strategy) for many different purposes -- from ethics, policies and education to security, technology and industry, the scope is broad and multi-faceted. If, like many others, you are unclear as to what this new terminology means, below is a diagram depicting the hierarchy of AI, machine learning and deep learning for you to consider. In healthcare, the opportunities are vast and significant. Just from a financial point of view, AI has the potential to bring material cost savings to the industry.

Is your enterprise machine ready? If it isn't, you need to act. Make no mistake, artificial intelligence (AI) technologies – such as machine learning (ML) and robotic processing automation (RPA) – are coming and coming fast. In this article and the two that follow, I will provide some thoughts about the impacts these new technologies will have on business and offer some suggestions to help your enterprise embrace the transition to a more automated future. The transition away from the manual processes of the 1950s to digital ones is well underway and in the near future we will enter the age of intelligent and autonomous processing.

Drones have been around for a few years now, but the truth is their usefulness largely has been limited to providing visual information to users in the form of photos and video captured by their onboard digital cameras. That's great if you're a photographer, need to inspect an inaccessible piece of equipment or some farm acreage, or require mobile surveillance. Other than that, commercial drones are little more than fun (and expensive) high-tech toys -- great for a cool selfie or a stunning aerial shot of the sunset, but not much more. But future iterations will be far more versatile. Drones that assist with operations are on the market, and this category is still growing. They complete tasks normally counted among the "three Ds"--dull, dirty, or dangerous, such as window washing.

Rapid innovation and productivity breakthroughs require an accelerated digital transformation strategy that melds people, business processes, advanced analytics, and new human/machine interaction technologies. Today, it is the supervised machine learning segment of AI that is generating the most economic value. But as digital transformation accelerates, the abundance of data that AI can consume will drive the speed of AI adoption even faster, including its unsupervised learning segment. We need only look at how quickly conversational AI (CAI) has become part of our everyday lives as we query Alexa, Siri or Cortana. But in the enterprise the interactions can be extremely complex, such as "Hey CAI, summarize the minutes and action items from the recording of the last board meeting."

The insurance industry consists of more than 7,000 companies that collect more than $1 trillion in premiums annually, providing fraudsters with huge opportunities to commit fraud using a growing number of schemes. Fraudsters are successful too often. According to FBI statistics, the total cost of non-health insurance fraud is estimated at more than $40 billion a year. Fighting fraud is like aiming at a constantly moving target, since criminals constantly hone and change their strategies. As insurers offer customers additional ways to submit information, fraudsters find a way to exploit new channels, and detecting issues is increasingly challenging because threats and attacks are growing in sophistication.