For Wachter, the “cutting costs and saving time” mindset that permeates AI’s development and deployment has led practitioners to focus almost exclusively on correlation, rather than causation, when building their models. “That spirit of making something quick and fast, but not necessarily improving it, also translates into ‘correlation is good enough – it gets the job done’,” she says, adding that the logic of austerity that underpins the technology’s real-world use means that the curiosity to discover the story between the data points is almost entirely absent. “We don’t actually care about the causality between things,” says Wachter. “There is an intellectual decline, if you will, because the tech people don’t really care about the social story between the data points, and social scientists are being left out of that loop.” She adds: “Really understanding how AI works is actually important to make it fairer and more equitable, but it also costs more in resources. There is very little incentive to figure out what is going on [in the models].” Taking the point further, McQuillan describes AI technology as a “correlation machine” that, in essence, produces conspiracy theories.
As the vast majority of the workforce has gone digital, organizations' core systems have been moving to the cloud. This accelerated cloud adoption has exponentially increased the use of third-party applications and the connections between systems and services, unleashing an entirely new cybersecurity challenge. There are three main factors that lead to the rise in app-to-app connectivity: Product-led growth (PLG): In an era of PLG and bottom-up software adoption, with software-as-a-service (SaaS) leaders like Okta and Slack; DevOps: Dev teams are freely generating and embedding API keys in. Hyperautomation: The rise of hyperautomation and low code/no code platforms means "citizen developers" can integrate and automate processes with the flip of a switch. The vast scope of integrations are now easily accessible to any kind of team, which means time saved and increased productivity. But while this makes an organization's job easier, it blurs visibility into potentially vulnerable app connections, making it extremely difficult for organizational IT and security leaders to have insight into all of the integrations deployed in their environment, which expands the organization's digital supply chain.
Interactions and learning trigger feelings and emotions. There is a need to develop emotional awareness, to pause and notice the emotional signals of the body. The practice of pause – the conscious allotting of space and time to look inwards and notice physical sensations like a ‘racing pulse’, a ‘shaking leg’ or a ‘clammy hand’ is a must for well-being. When things seem to be falling apart, it is useful to breathe. Evidence suggests that, by counting our breaths and centring our breathing, we calm our minds. Whether dealing with difficult conversations with colleagues, family, friends, teachers or students, the ability to regulate emotion and attention is a well-being practice proven to mitigate accompanying anxiety, fear, anger or despair. ... Feeling a pit in one’s stomach or a thumping heart are physical symptoms that often accompany intense emotional responses. At such times, a friend; app; conscious trained practice like counting numbers, breaths or tiles on the floor; time-out or break; or walking can all be good ways to physically distract focus and allow some of the intensity of the emotion to diminish.
For many forms of automation, deskilling isn’t a serious problem. Knowledge workers in general, including ops personnel, may face many routine, repeatable tasks in their day-to-day work that don’t require a level of skill that would cause an issue if that skill were lost. All such routine tasks are subject to automation without concern. At the other extreme, organizations may aspire to "lights out" production environments, so fully automated that there’s no reason to keep the lights on, because there are no people on duty. Any organization with such a lights out environment is likely to lose any staff who might be able to fix something if it goes wrong, either via deskilling or attrition. As AI-based automation becomes increasingly sophisticated, therefore, organizations will reach some optimal point where the advantages of automation sufficiently balance any disadvantages. Finding this optimum depends upon the people involved — the skilled workers who must somehow accommodate automation in their day-to-day work. Be sure to listen to the senior-level people who are adept at analogizing. They can solve problems that automation will never be able to solve.
A product mindset is about delivering things that provide value to our users, within the context of the organisation and their strategy, and do so sustainability (i.e. balancing the now and the future). For the purpose of this article, I will use product thinking, product mindset and product management very much interchangeably. ... In practice this means achieving product-market-fit by balancing what our users need, want and find valuable (desirability), what we need to achieve (and can afford) as an organisation (viability) and what is possible technically, culturally, legally, etc (feasibility), and doing this without falling into the trap of premature optimisation or closing options too early. To give a tiny, very specific, but quite telling example: for the medical device organisation we chose Bash as scripting language because the DevOps lead was comfortable with it. Eventually we realised that the client’s engineers had no Bash experience, but as a .Net shop were far more comfortable with Python. Adding a user-centric approach which is part of a product mindset at an early stage would have prevented this mistake and the resulting rework.
Last year, MIT researchers announced that they had built “liquid” neural networks, inspired by the brains of small species: a class of flexible, robust machine learning models that learn on the job and can adapt to changing conditions, for real-world safety-critical tasks, like driving and flying. The flexibility of these “liquid” neural nets meant boosting the bloodline to our connected world, yielding better decision-making for many tasks involving time-series data, such as brain and heart monitoring, weather forecasting, and stock pricing. But these models become computationally expensive as their number of neurons and synapses increase and require clunky computer programs to solve their underlying, complicated math. And all of this math, similar to many physical phenomena, becomes harder to solve with size, meaning computing lots of small steps to arrive at a solution. Now, the same team of scientists has discovered a way to alleviate this bottleneck by solving the differential equation behind the interaction of two neurons through synapses to unlock a new type of fast and efficient artificial intelligence algorithms.
Microservices are everywhere in today’s increasingly virtual, decentralized world. 85% of organizations with 5,000 or more employees use microservices in their organization in some capacity as of 2021. Even more tellingly, 0% report having no intention of adopting microservices in their company. Clearly, microservices are here to stay, meaning more and more businesses will be adopting microservices in the coming months and years. This is good news, as microservices are capable of so much. This popularity comes with its own risks, though. Some businesses that integrate microservices into their existing workflow will need help figuring out what to do with them. ... Uber would not be able to exist if not for microservices. Although very brief, their monolithic structure resulted in insurmountable hurdles to their growth. Without microservices, the ride-sharing app couldn’t fix bugs when they occurred, develop or launch new services, or transition to a global marketplace. Their monolithic structure was prohibitively complex, requiring developers to have extensive experience working with the system to make even simple changes.
It’s easy to get caught up in the tactical work, so I make sure to reserve time for high-level plans and strategy. This means following tech news and blogs to stay abreast of the latest in technology trends, keeping an eye on market news, reading the latest analyst research, meeting ups with my peers in our private equity portfolio companies, and more. It’s important for me not just to be on top of the technology, but to understand where we’re taking the business in the future. This kind of thinking is what helped lead to Gartner positioning Boomi as a Leader in the 2021 Gartner Magic Quadrant for Enterprise Integration Platform as a Service (EiPaaS) for eight consecutive years. ... If you’re thinking of getting into software engineering, here’s my advice: Just do it. It’s a high-demand career, and there is a continual lack of strong talent in the industry. You’ll find almost limitless opportunities once you get started. And there has never been a better time to do so, with many technological advancements that have lowered barriers to entry. As you move into management, though, it’s important to remember that your job is no longer to code – it’s to satisfy customer requirements and meet business goals.
A key consideration for large organizations is ensuring that no major component can function with complete autonomy or in a silo. You don't want a squad to be incapable of getting things done without any input from other squads. Quite the contrary, for minor decisions or implementations, each squad is empowered to move as quickly as possible. For major decisions with little or no recourse to make changes later, collaboration is key to the "measure twice, cut once" approach to critical decision-making. This enforced "checks and balances" means that no one chapter can unilaterally stray too far outside our strategic bounds. At a lower level, the Delivery Management squad team members work within other squads but all report to the same manager. Embedding them within other teams helps make every squad's activities as consistent as possible. This allows minimal impact on sprints when, for example, a delivery manager is out on leave, because process alignment is a goal within the Delivery Management function.
CIOs need to be asking questions to their teams to assess this potential exposure and understand the risk, as well as putting plans in place to address it. Fortunately, recent breakthroughs have been able to eliminate this encryption gap and maintain full protection for private keys. Leading CPU vendors have added security hardware within their advanced microprocessors that prevents any unauthorized access to code or data during execution or afterwards in what remains in memory caches. The chips are now in most servers, particularly those used by public cloud vendors, involving a technology generally known as confidential computing. This “secure enclave” technology closes the encryption gap and protects private keys, but it has required changes to code and IT processes that can involve a significant amount of technical work. It is specific to a particular cloud provider (meaning it must be altered for use in other clouds) and complicates future changes in code or operational processes. Fortunately, new “go-between” technology eliminates the need for such modifications and potentially offers multi-cloud portability with unlimited scale.
Quote for the day:
"The leadership team is the most important asset of the company and can be its worst liability" -- Med Jones