Rapid advances in artificial intelligence (AI) have made this technology important for many industries, including finance, energy, healthcare, and microelectronics. AI is driving a multi-trillion-dollar global market while helping to solve some tough societal problems such as tracking the current pandemic and predicting the severity of climate-driven events like hurricanes and wildfires. Today, AI algorithms are primarily run at large data centers, that is in the cloud. For this intelligence to be used at the edge, data must be transmitted to the cloud, analyzed there, and the results transmitted back to the edge – a device in the field of operation, whether it is a sensor tracking the strength of a bridge, a mobile phone, a medical implant, or an autonomous vehicle. The problem with the current approach of using AI primarily in the cloud is that it consumes much energy and can introduce data transmission delays and security vulnerabilities.

Rapid advances in artificial intelligence (AI) have made this technology important for many industries, including finance, energy, healthcare, and microelectronics. AI is driving a multi-trillion-dollar global market while helping to solve some tough societal problems such as tracking the current pandemic and predicting the severity of climate-driven events like hurricanes and wildfires. Today, AI algorithms are primarily run at large data centers, that is in the cloud. For this intelligence to be used at the edge, data must be transmitted to the cloud, analyzed there, and the results transmitted back to the edge – a device in the field of operation, whether it is a sensor tracking the strength of a bridge, a mobile phone, a medical implant, or an autonomous vehicle. The problem with the current approach of using AI primarily in the cloud is that it consumes much energy and can introduce data transmission delays and security vulnerabilities.

Artificial Intelligence (AI) has the capability to provide radiologists with tools to help improve their productivity and decision making, possibly leading to quicker diagnosis and improved patient outcomes. As evidenced by the great number of vendors entering the market, it is initially deploying as a diverse collection of assistive applications and tools. These are allowing radiologists to augment, quantify and stratify the information available to them and has the promise to provide major opportunities to enhance and augment the radiology reading and richness of the resulting reports. It is also improving access to medical record information with the goal of helping to give radiologists more time to think about what is going on with patients, diagnose more complex cases, collaborate with patient care teams, and perform more invasive procedures. Deep Learning algorithms in particular have promise to transform the foundation for decision making and workflow, as these types of algorithms have the ability to "learn" by example to execute a task as well as interpret new data.

The rapid growth of digital technologies in the recent years has created a very distinct type of a customer-a customer that allots majority of his/her time on the internet be it via social media networks or mobile. We, our self can identify with them the way we spend most of our time online- and this is majorly affecting the way we communicate with customers. Statista says that as of 2018, "over 4 billion people are active internet users and 3.3 billion are social media users". Plus, out of this digital population, 79% of consumers demand real-time conversations and engagements with brands rather than phone or e-mail. The new type of customer is empowered with all the information and connectivity at their disposal.

Artificial Intelligence (AI) has the capability to provide radiologists with tools to improve their productivity, decision making and effectiveness and will lead to quicker diagnosis and improved patient outcomes. It will initially deploy as a diverse collection of assistive tools to augment, quantify and stratify the information available to the diagnostician, and offer a major opportunity to enhance and augment the radiology reading. It will improve access to medical record information and give radiologists more time to think about what is going on with patients, diagnose more complex cases, collaborate with patient care teams, and perform more invasive procedures. Deep Learning algorithms in particular will form the foundation for decision and workflow support tools and diagnostic capabilities. Algorithms will provide software the ability to "learn" by example on how to execute a task, then automatically execute those tasks as well as interpret new data.

As I discussed in my previous blogs, artificial intelligence in the form of cognitive computing, robotic processing automation and other technologies presents insurers with many new opportunities for efficiency and for accelerated growth. The question for insurers is whether to use AI to automate processes or to augment the workforce and make it more creative and effective. Many insurers are adapting AI to automate basic underwriting transactions and renewal processing. AI also provides consistent, low-cost performance in making underwriting eligibility decisions. Insurers are exploring the use of AI to augment the expert workforce in areas including risk management, client and/or prospect discovery, coverage recommendations and fraud detection.