A surefire way to shoot yourself in the foot when you're leading a large-scale change effort is to ignore what's on the minds of your employees. In research we conducted for our recently published book, Beyond Performance 2.0 (John Wiley & Sons, July 2019), we found that executives at exactly zero companies that disregarded an analysis of employee mind-sets during a change program rated the transformation as "extremely successful." Conversely, executives at companies that took the time and trouble to address mind-sets were four times more likely than those that didn't to rate their change programs as at least "successful." Executives at companies that took the time and trouble to address mind-sets were four times more likely than those that didn't to rate their change programs as at least "successful." Those numbers reflect the power of mind-set shifts. In human systems, they help to achieve the same effect as the transformation of a caterpillar into a butterfly or a tadpole into a frog: when employees become open to new ways of looking at what's possible for them and their organization, they can never return to a state of not having that broader perspective, just as butterflies and frogs can't revert to their previous physical forms.

If you've ever been to an expensive restaurant and ordered a familiar dish like, say, lasagna, but received a plate with five different elements arranged in a way that does not at all resemble what you know as lasagna, then you have probably tasted deconstructionism. This approach to cuisine aims to challenge the way our brain makes associations, to break existing patterns of interpretation and, in so doing, to release unrealized potential. If the different elements work together harmoniously, it should be the best lasagna you've ever tasted. In principle, the 5th Generation network is deconstructed. Firstly, with its Service-Based Architecture (SBA) the core of the network is a mesh of interconnected services, each working independently but collaboratively.

New advances in technology have pushed many sectors, including finance, to start their digital transformation. Some of the emerging FinTech trends today promise to save time and eliminate headaches from the industry, such as fraudulent activity. The change, driven by breakthroughs in artificial intelligence (AI) and machine learning, will transform the financial services landscape and forever change the future of FinTech. And those who want to remain competitive will need to reinvent their business processes as quickly as possible. Although the adoption of AI tech in finance is accompanied by several challenges, Microsoft's Accelerating Competitive Advantage with AI report reveals that global AI investments in the financial sector will reach a value of $5.6 billion in 2019.

Machine learning technologies and techniques are giving organizations powerful new ways to utilize the vast amounts of data they're collecting. According to several reports, ML spending is increasing at a compound annual growth rate (CAGR) of around 25%. That's benefitting vendors providing ML solutions, which appears to be mostly cloud vendors outside of the HPC segment. According to Zion Market Research's July report, the global market for ML was valued at $1.6 billion in 2017 and is expected to account for $20.8 billion in spending by 2024, which translates into a rather healthy 44% compound annual growth rate (CAGR). That was the outlier in a recent roundup of ML market reports. Market Reports World came up with a similar number in its global tally on ML spending.

The scarcity of health care resources is a long-standing, persistent global issue that is increasing with the worldwide aging population [1]. Possible approaches toward alleviating this scarcity include applying a sharing economy model to the health care industry [2]. The concept of sharing has been incorporated into a range of commercial activities related to daily life, such as retail and transportation. The health care system has also been influenced by the globally growing trend toward a sharing economy [3] and will likely advance with these trends in the near future. Such foreseeable trends continually accompany the integration of innovative technology in the emerging big-data era, including artificial intelligence (AI). Decision making on global issues requires new technologies based on AI techniques [4].

If you do know what a Data Scientist is, you are rare to find, as since even the most experienced professionals still have difficulty defining the scope of the area. One possible delimitation is that the data scientist is the person responsible for producing predictive and / or explanatory models using machine learning and statistics.

Since I spoke at techUK's Digital Ethics 2018 conference the conversation on AI has continued to grow. Research that we recently conducted showed that UK organisations have been increasing their adoption of AI technologies over the past year. The number of companies who now state they have an AI strategy in place has more than doubled – from 11% in 2018 to 24% today, with over half of the organisations reported to be using AI to some degree, indicating that AI is increasingly becoming more accessible. The rise in AI technologies creates more urgency for organisations to understand the implications of AI empowered decision making and how to ensure AI is being used responsibly. However, many UK leaders lack an understanding of how AI can be used in a fair, responsible and effective way with two-thirds (63%) not knowing how AI systems reach conclusions.

Machines that make decisions about us: what could possibly go wrong? Essays, speeches, seminars pose that question year after year as artificial intelligence research makes stunning advances. Baked-in biases in algorithms are only one of many issues as a result. Jonathan Shaw, managing editor, Harvard Magazine, wrote earlier this year: "Artificial intelligence can aggregate and assess vast quantities of data that are sometimes beyond human capacity to analyze unaided, thereby enabling AI to make hiring recommendations, determine in seconds the creditworthiness of loan applicants, and predict the chances that criminals will re-offend." Again, what could possibly go wrong?

"Rather than questioning the character of thinking machines, futurist Amy Webb turns a critical eye on the humans behind the computers. With AI's development overwhelmingly driven by nine tech powerhouses, she asks: Is it possible for the technology to serve the best interests of everyone?"―Wired "Webb's assessments are based on analyses of patent filings, policy briefings, interviews and other sources. She paints vivid pictures of how AI could benefit the average person, via precision medicine or smarter dating apps...Her forecasts are provocative and unsettlingly plausible."―Science News "Instead of predicting the future, Webb lays out scenarios for optimistic, pragmatic, and catastrophic outcomes -- all extrapolated from current facts. However impractical you may find the idea of a common Apple-Amazon operating system named Applezon, considering potential scenarios is a fantastically healthy exercise, because anyone who tells you they know how AI is going to turn out is lying."―VentureBeat

The Apache Software Foundation (ASF) recently announced that SINGA, a framework for distributed deep-learning, has graduated to top-level project (TLP) status, signifying the project's maturity and stability. SINGA has already been adopted by companies in several sectors, including banking and healthcare. Originally developed at the National University of Singapore, SINGA joined ASF's incubator in March 2015. SINGA provides a framework for distributing the work of training deep-learning models across a cluster of machines, in order to reduce the time needed to train the model. In addition to its use as a platform for academic research, SINGA has been used in commercial applications by Citigroup and CBRE, as well as in several health-care applications, including an app to aid patients with pre-diabetes.