Generative AI is revolutionising how we experience the internet and the world around us. Global AI investment surged from $12.75 million in 2015 to $93.5 billion in 2021, and the market is projected to reach $422.37 billion by 2028. While this outlook might make it sound like generative AI is the "silver bullet" for pushing our global society forward, it comes with an important footnote: The ethical implications are not yet well-defined. This is a severe problem that can inhibit continued growth and expansion.

As the adage goes, a workman is only as good as his tools. There is no disputing that, but you can never overlook the power of qualification, aptitude, and experience when it comes to data quality. You need to select a data quality team that is acquainted with the high dynamism of the digital world and is up to date with contemporary data management tools and techniques. The data steward or the data architect or the data leadership/management team should understand both the IT and business aspects of the whole arrangement for a more harmonious strategy. One should understand the objectives of the organization, the type of business in, the demanding market conditions plus the impact of data across these and be conversant with the big picture.

Quantum computing and AI can be combined to have an even greater impact. The goal of this blog post is to introduce basic concepts of quantum computers and try to demystify them. Quantum computers use properties of matter observed at the micro (sub-atomic) level to perform computations and solve problems. Progress in quantum computers will have significant implications in the near future. It could render current encryption standard of the Internet useless, reduce drug development time, transform AI and much more.

Businesses and corporations are increasingly interested in applying artificial intelligence (AI) to their operations, but see themselves falling behind in implementing AI, according to a research survey report from Talkdesk with relevance for insurtech. Insurance has a "heightened challenge of being able to get organizational life and culture driving towards the adoption and embracing of AI," said Antonio Gonzalez, senior manager, industries and AI research at Talkdesk. Talkdesk surveyed 500 customer experience professionals from the U.S., Canada, France, Germany and the U.K., with about 7% of respondents from the financial services sector including insurance.

Improving digital touchpoints with policyholders and "giving the client what they want" is essential to stop them "finding somewhere else to go", said James Woollam, managing director at Hayes Parsons Insurance and Risk Management. Speaking as part of a panel entitled How tech should support MGAs and brokers at the Managing General Agents' Association's (MGAA) 2022 conference today (29 June 2022), Woollam explained that improving consumers' experience of interacting with insurance firms was "do or die".

With the likely development of superintelligent programs in the near future, many scientists have raised the issue of safety as it relates to such technology. A common theme in Artificial Intellgence (AI) safety research is the possibility of keeping a super-intelligent agent in a sealed hardware so as to prevent it from doing any harm to humankind. In this essay we will review specific proposals aimed at creating restricted environments for safely interacting with artificial minds. We will evaluate feasibility of presented proposals and suggest a protocol aimed at enhancing safety and security of such methodologies. While it is unlikely that long-term and secure confinement of AI is possible, we are hopeful that the proposed protocol will give researchers a little more time to find a permanent and satisfactory solution for addressing existential risks associated with appearance of super-intelligent machines.

Much has changed in the AI/ML world but the concept of'garbage in; garbage out' remains stoic. Any algorithm is only as good as its training data. And, no training data is without bias, not even the ones generated through automation. In the past, many machine learning algorithms have been unfair to certain religions, races, genders, ethnicities, and economical statuses, among others. The Watson supercomputer from IBM that gave suggestions to doctors using a dataset of medical research papers was found to favor reputable studies only.

All workers, and especially those on the frontline, deserve and need mentorships, training, and career guidance. If they receive that type of attention, their wages increase and they can have exciting career pathways with a higher loyalty. Frontline workers in the United States – truck drivers, manufacturing line workers, packers and shippers, grocery clerks, servers, healthcare assistants, housekeepers, janitors and so on – are frequently trapped in positions with low wages and little to no prospects for advancement. However, if these services gradually become more important, this could eventually change. What if we told you we could speed it up by investing now?

In machine learning, understanding why a model makes certain decisions is often just as important as whether those decisions are correct. While tools exist to help experts make sense of a model's reasoning, often these methods only provide insights on one decision at a time, and each must be manually evaluated. Models are commonly trained using millions of data inputs, making it almost impossible for a human to evaluate enough decisions to identify patterns. Now, researchers at MIT and IBM Research have created a method that enables a user to aggregate, sort, and rank these individual explanations to rapidly analyze a machine-learning model's behaviour. Their technique, called Shared Interest, incorporates quantifiable metrics that compare how well a model's reasoning matches that of a human.