Those with high motivation potential showed resilience and confidence in their capacity to lead; those who scored lower on this dimension were less likely to persevere when faced with new and unknown situations. Those who possessed strong people potential were empathetic and more adept at building relationships than their less people-savvy peers. And leaders with high change potential were able to move out of their comfort zones to experiment and take necessary risks; those who were more averse to change had more difficulty going against the status quo. ... Too many organizations eliminate talented leaders from consideration because the criteria used to determine potential are subjective and inconsistent. If created carefully, a clear, consistent definition of leadership potential can reduce the potential for bias, increase diversity, and save money by ensuring that the organization invests in high-potential employees early in their careers.
Jobs all across the business and finance landscape will be heavily affected in the manner of insurance underwriters: book keepers, accountants, auditors, loan officers, tellers, clerks, and postal service workers will easily be replaced by artificial intelligence. The legal profession is another highly populated sector that will have a difficult time as the need for secretaries, paralegals and court reporters will decline. And if experts are correct in their projections, the very top business leaders might not be immune either. Jack Ma from Alibaba, recently said that CEOs themselves could be on the chopping block, going so far as to predict that "In 30 years, a robot will likely be on the cover of Time Magazine as the best CEO." Ma paints a bleak picture of what the three transitional decades could look like for those who are “unprepared for the upheaval technology is set to bring.”
In 2017 the U.S. employs nearly 780,000 people in cybersecurity positions, with approximately 350 000 current cybersecurity openings, according to CyberSeek, a project supported by the National Initiative for Cybersecurity Education (NICE), a program of the National Institute of Standards and Technology (NIST) in the U.S. Department of Commerce. The current number of U.S. cybersecurity job openings is up from 209,000 in 2015. At that time, job postings were already up 74 percent over the previous five years, according to a Peninsula Press analysis of numbers from the Bureau of Labor Statistics. Security starts at the top. Right now, about 65% of large U.S. companies have a CISO (Chief Information Security Officer) position, up from 50% in 2016, according to ISACA, an independent, nonprofit, global association.
Cybersecurity incidents regularly hit the headlines, the WannaCry ransomware outbreak in mid-May being a particularly high-profile example. It says a lot about the current state of cybersecurity that the escalation of ransomware had been widely predicted, that the crude but effective WannaCry attack could easily have been defended, and that the perpetrators -- despite the attentions of multiple security firms and government agencies -- remain undiscovered (at the time of writing). Talking of predictions, at the start of the year ZDNet's sister site Tech Pro Research examined 345 cybersecurity predictions for 2017 from 49 organisations, assigning them among 39 emergent categories. Here's the ranking of topics that cybersecurity experts were worried about six months ago:
The securitization of contracts involves creating standardized products fromlarge-scale, nonstandard contractual agreements between two parties. Examples include agreements regarding the long-term off-take of LNG and structured investment products, including those based on energy consumption patterns. Many traders in less-developed commodity markets have created a business based on the securitization of contracts. Their business model will face growing pressure as commodity markets become increasingly developed and as more short-term markets emerge, offering greater liquidity, price transparency, and ability to hedge risk. That evolution is already evident in a number of markets, including European gas. In addition to the risks for traders, however, there will also be opportunities.
In recent years we have seen a surge of this kind of design thinking in business and a drive in governments to apply design principles to policy. An insurgency of Innovation Labs have sprung up like Sitra in Finland, Mindlabin Denmark, 18F in the US, and Policy Lab in the UK. Experimentation, prototyping, and openness underpin this way of operating which puts users first and brings in agile methods from tech and design communities to innovate in new ways. To encourage this, public institutions and charitable foundations have opened up challenge prizes to stimulate markets and promote design-led innovation. Social impact investment funds and incentives like the industrial strategy challenge fund in the UK now seek to drive innovation further.
When kids in college take a course called “Data Structures,” they get to learn what life was like when their grandparents wrote code and couldn’t depend on the existence of a layer called “the database.” Real programmers had to store, sort, and join tables full of data, without the help of Oracle, MySQL, or MongoDB. Machine learning algorithms are a few short years away from making that jump. Right now programmers and data scientists need to write much of their own code to perform complex analysis. Soon, languages like R and some of the cleverest business intelligence tools will stop being special and start being a regular feature in most software stacks. They’ll go from being four or five special slides in the PowerPoint sales deck to a little rectangle in the architecture drawing that’s taken for granted.
“We have a situation where these artificial intelligence systems may be perpetuating historical patterns of bias that we might find socially unacceptable and which we might be trying to move away from.” While many people would assume that artificial intelligence algorithms are objective tools making objective calculations, the fact is these tools are created from and trained on large sets of data (images, text, video, etc.) that currently exist online. This is data that has been created by humans, and thus is data that’s not free from bias. When AI algorithms and content intersect, we need to be careful about the results. The danger with overuse of artificial intelligence in marketing is that our dominant, biased discourses will remain dominant and biased, especially if we assume an AI tool is taking an objective tack.
IoT solutions affect multiple teams within the organization. Partner with these affected teams early in the planning process to get their requirements, gain their support (knowledge, resources, and budget), and leverage their influence to remove barriers during the execution stages. Partner with your organization’s digital transformation or innovation office, if one exists. Equally important, partner with IoT solution vendors throughout the process. At this stage of the market, their solutions are still evolving. Work with your IoT vendor at a deeper level than you would with other vendors. Stay in close contact and leverage their product management and technical support teams throughout the project. Co-design the solution and project with them – tell them what features you like to see, report bugs, and test updated versions of the product.
By contrast, humans “learn from very few examples, can do very long-term planning, and are capable of forming abstract models of a situation and [manipulating] these models to achieve extreme generalization.” Even simple human behaviors are laborious to teach to a deep learning algorithm. Let’s examine a situation such as avoiding being hit by a car as you walk down the road. If you go the supervised learning route, you’d need huge data sets of car situations with clearly labeled actions to take, such as “stop” or “move.” Then you’d need to train a neural network to learn the mapping between the situation and the appropriate action. If you go the reinforcement learning route, where you give an algorithm a goal and let it independently determine the ideal actions to take, the computer would need to die thousands of times before learning to avoid cars in different situations.
Quote for the day:
"Leadership is not a solo sport; if you lead alone, you are not leading." -- D.A. Blankinship