Not surprisingly, the ability to work remotely has been a critical factor. Forty-four percent of respondents cited Business Continuity Plans as a key factor. Several customers have told us, however, that their business continuity plans were far too focused on disaster recovery and as such they made tactical investments to shore up their digital capabilities. C-suite backing and budget flexibility were cited as major factors. We see this as a real positive in that the corner office and boards of directors are tuned into digital. They understand the importance of getting digital “right” and we believe that they now have good data from the past 10 months on which investments will yield the highest payback. As such, we expect further funding toward digital initiatives. Balance sheets are strong for many companies as several have tapped corporate debt and taken advantage of the low interest rate climate. Twenty-seven percent cited the use of emerging technologies as a factor. Some of these, it could be argued, fall into the first category – working remotely. The bottom line is we believe that the 10-month proof of concept that came from COVID puts organizations in a position to act quickly in 2021 to accelerate their digital transformations further by filling gaps and identifying initiatives that will bring competitive advantage.
“Data analytics is a good place to start with any transformation, to make sound decisions and design the proper solutions,” says Carol Lynn Thistle, managing director at CIO executive recruiting firm Heller Search Associates. One foundational IT position is the enterprise data architect or (in some cases) a chief data officer. These highly skilled professionals can look at blueprints, align IT tooling with information assets, and connect to the business strategy, Thistle explains. ... “Digital transformation is about automation of business processes using relevant technologies such AI, machine learning, robotics, and distributed ledger,” says Fay Arjomandi, founder and CEO of mimik Technology, a cloud-edge platform provider. “Individuals with business knowledge that can define the business process in excruciating detail. This is an important role, and we see a huge shortage in the market.” ... “[Organizations need] a digitally savvy person at the CXO level who will help other executives buy into the culture change that will be required to truly transform the organization into one that is digital-first,” says Mike Buob, vice president of customer experience and innovation for Sogeti, the technology and engineering services division of Capgemini.
Jiuzhang, as the supercomputer is called, has outperformed Google’s supercomputer, which the company had claimed last year to have achieved quantum computing supremacy. The supercomputer by Google named Sycamore is a 54-qubit processor, consisting of high-fidelity quantum logic gates that could perform the target computation in 200 seconds. The researchers explored Boson sampling, a task considered to be a strong candidate to demonstrate quantum computational advantage. As the researcher cited in the research paper, they performed Gaussian boson sampling (GBS), which is a new paradigm of boson sampling, one of the first feasible protocols for quantum computational advantage. In boson sampling and its variants, nonclassical light is injected into a linear optical network, which generates highly random photon-number, measured by single-photon detectors. Researchers sent 50 indistinguishable single-mode squeezed states into a 100-mode ultralow-loss interferometer with full connectivity and random matrix. They further shared that the whole optical setup is phase-locked and that the sampling of output was done using 100 high-efficiency single-photon detectors.
The Edge basically means “not Cloud” because what constitutes the Edge can differ depending on the application. To explain, let’s look at an example. In a hospital, you might want to know the location of all medical assets (e.g., IV pumps, EKG machines, etc.) and use a Bluetooth indoor tracking IoT solution. The solution has Bluetooth Tags, which you attach to the assets you want to track (e.g., an IV pump). You also have Bluetooth Hubs, one in each room, that listens for signals from the Tags to determine which room each Tag is in (and therefore what room the asset is in). In this scenario, both the Tags and the Hubs could be considered the “Edge.” The Tags could perform some simple calculations and only send data to the Hubs if there’s a large sensory data change. ... One of the issues with the term ”IoT” is how broadly it’s defined. Autonomous vehicles that cost tens of thousands of dollars collect Terabytes of data and use 4G cellular networks are considered IoT. At the same time, sensors that cost a couple of dollars collect just bytes of data and use Low-Power Wide-Area Networks (LPWANS) are also considered IoT. The problem is that everyone is focusing on high bandwidth IoT applications like autonomous vehicles, the smart home, and security cameras.
When asked about the dangers of AI, Arman asserted that ‘danger has always existed in every technological innovation in history, from the ever-increasing trail of pollution caused by the first Industrial Revolution to the idea of Nuclear power generation to free use of pesticides everywhere into genetic modification of food and so on.’ AI is only a part of that as ‘it is on its path to outgrow human’s capacity to fully understand how it makes decisions and what is the base of its outcomes.’ Indeed, this would be the first time that our intellectual superiority would be taken away. To shed some light on this, Arman retells a conversation he had with one AI lead from key players in Silicon Valley during a meeting in 2017: ‘After 2 hours of discussing, brainstorming and trying to picture a path, we ended up having no firm idea on where AI was leading us. The final outcome was that each individually announced that they believe it is too early to predict anything and we can’t even say with certainty where we will be in 18 months. They also refused to acknowledge the risk that was brought up through research from my team projecting that – back in 2017, even with AI still being in its infancy – it had the ability to take away over 1 billion jobs across the globe.
FP and Object-Oriented Programming (OOP) aren’t really at odds with each other, at least not if you use each as if they were a tool rather than a lifestyle. In FP, you generally try to cleanly separate your data definitions from functionality that operates on. In OOP, you’re encouraged to combine them and blur the differences between them. Both can be incredibly helpful depending on what you’re doing. For example, in the F# language we encourage the use of objects to encapsulate data and expose functionality conveniently. That’s a far cry from encouraging people to model everything using inheritance hierarchies, and at the end of the day you still tend to work with an object in a functional way, by calling methods or properties that just produce outputs. Both styles can work well together if you don’t “all in” on one approach or the other. ... What’s interesting is that even though F# runs on .NET, which often has an “enterprisey” kind of reputation, F# itself doesn’t really suffer the negative aspects of that kind of reputation. It can be used for enterprise work, but it’s usually seen as lightweight and its community is engaged and available as opposed to stuck behind a corporate firewall.
Teams may take different routes to arrive at a microservice architecture, but they tend to face a common set of challenges once they get there. John Laban, CEO and co-founder of OpsLevel, which helps teams build and manage microservices told us that “with a distributed or microservices based architecture your teams benefit from being able to move independently from each other, but there are some gotchas to look out for.” Indeed, the linked O’Reilly chart shows how the top 10 challenges organizations face when adopting microservices are shared by 25%+ of respondents. While we discussed some of the adoption blockers above, feedback from our interviews highlighted issues around managing complexity. The lack of a coherent definition for a service can cause teams to generate unnecessary overhead by creating too many similar services or spreading related services across different groups. One company we spoke with went down the path of decomposing their monolith and took it too far. Their service definitions were too narrow, and by the time decomposition was complete, they were left with 4,000+ microservices to manage. They then had to backtrack and consolidate down to a more manageable number.
The key to adaptability, virtual collaboration, and digital transformation (and agile) is distributed leadership and self-managed teams. This requires that everyone have core leadership skills, and not just people in the positions of managers and above. For the past 11 years, I’ve been training and coaching IT professionals at every job level – from individual contributors up to CIOs – in what I believe are the six key core leadership skills that every IT professional needs to master, even more so today than at any time in the past. ... "Yes, IT professionals need to know the underpinnings of technology and tech trends. But what many fail to realize is how heavily IT leaders rely on effective communication skills to do their jobs successfully. As CIO of ServiceNow, my role demands clear, consistent communication – both within my organization and across other functions – to make sure that everyone is aligned on the right outcomes. Communication is the key to digital transformation and IT professionals need to communicate with employees across departments on what this means for their work.” - Chris Bedi, CIO, ServiceNow
As you look at the amount of productive time data scientists spend creating value, that can be pretty small compared to their non-productive time — and that’s a concern. Part of the non-productive time, of course, has been with those data scientists having to discover a model and optimize it. Then they would do the steps to operationalize it. But maybe doing the data and operations engineering things to operationalize the model can be much more efficiently done with another team of people who have the skills to do that. We’re talking about specialization here, really. But there are some other learnings as well. I recently wrote a blog about it. In it, I looked at the modern Toyota production system and started to ask questions around what we could learn about what they have learned, if you like, over the last 70 years or so. It was not just about automation, but also how they went about doing research and development, how they approached tooling, and how they did continuous improvement. We have a lot to learn in those areas. For an awful lot of organizations that I deal with, they haven’t had a lot of experience around such operationalization problems. They haven’t built that part of their assembly line yet.
What is neuromorphic computing? Everything you need to know about how it is changing the future of computing
First, to understand neuromorphic technology it make sense to take a quick look at how the brain works. Messages are carried to and from the brain via neurons, a type of nerve cell. If you step on a pin, pain receptors in the skin of your foot pick up the damage, and trigger something known as an action potential -- basically, a signal to activate -- in the neurone that's connected to the foot. The action potential causes the neuron to release chemicals across a gap called a synapse, which happens across many neurons until the message reaches the brain. Your brain then registers the pain, at which point messages are sent from neuron to neuron until the signal reaches your leg muscles -- and you move your foot. An action potential can be triggered by either lots of inputs at once (spatial), or input that builds up over time (temporal). These techniques, plus the huge interconnectivity of synapses -- one synapse might be connected to 10,000 others -- means the brain can transfer information quickly and efficiently. Neuromorphic computing models the way the brain works through spiking neural networks. Conventional computing is based on transistors that are either on or off, one or zero.
Quote for the day:
"Every great leader has incredible odds to overcome." -- Wayde Goodall