Three Amigos meetings remove uncertainty from development projects, as they provide a specified time for everyone to get on the same page about what to -- or not to -- build. "The meeting exposes any potential assumptions and forces explicit answers," said Jeff Sing, lead software QA engineer at Optimizely, a digital experience optimization platform. "Everyone walks away with crystal-clear guidelines on what will be delivered and gets ahead of any potential scope creep." For example, a new feature entails new business requirements, engineering changes, UX flow and design. Each team faces its own challenges and requirements. The business requirements focus on a broad problem space, and how to monetize the product. The engineering requirements center on the technical solution and hurdles. The UX requirements define product usability. The design requirements ensure the product looks finished. All of these requirements might align -- or they might not. "This is why a formalized meeting needs to occur to hash out how to achieve everyone's goals, or which requirements will not be met and need to be dropped in order to build the right product on the right time schedule," Sing said.
For an intelligent automation programme to really deliver, a strategy and purpose is needed. This could be improving data quality, operational efficiency, process quality and employee empowerment, or enhancing stakeholder experiences by providing quicker, more accurate responses. Whatever the rationale, an intelligent automation strategy must be aligned to the wider needs of the business. Ideally, key stakeholders should be involved in creating the vision; if they haven’t, engage them now. If they see intelligent automation as a strategic business project, they’ll support it and provide the necessary financial and human resources too. Although intelligent automation is usually managed by a business team, it will still be governed by the IT team using existing practices, so they must also be involved at the beginning. IT will support intelligent automation on many critical fronts, such as compliance with IT security, auditability, the supporting infrastructure, its configuration and scalability. So intelligent automation can scale as demand increases, plan where it sits within the business. A centralised approach encompasses the entire organisation, so it may be beneficial to embed this into a ‘centre of excellence’ (CoE) or start moving towards creating this operating environment.,/div.
Quote for the day:
"Leadership is about carrying on when everyone else has given up" -- Gordon Tredgold
A common mistake companies make is creating and deploying AI models using Agile approaches fit for software development, like Scrum or DevOps. These frameworks traditionally require breaking down a large project into small components so that they can be tackled quickly and independently, culminating in iterative yet stable releases, like constructing a building floor by floor. However, AI is more like a science experiment than a building. It is experiment-driven, where the whole model development life cycle needs to be iterated—from data processing to model development and eventually monitoring—and not just built from independent components. These processes feed back into one another; therefore, a model is never quite “done.” ... We know AI requires specialized skill sets—data scientists remain highly sought-after hires in any enterprise. But it’s not just the data scientists who build the models and product owners who manage the functional requirements who are necessary in order for AI to work. The emerging role of machine-learning engineer is required to help scale AI into reusable and stable processes that your business can depend on. Professionals in model operations (model ops) are specialized technicians who manage post-deployment model performance and are ultimately responsible for ongoing stability and continuity of operations.
The necessity to privately provision cyber security has resulted in a significant gap between the demand for cyber security professionals and the supply of professionals with appropriate skills. Multiple studies have identified cyber security as the domain with one of the highest skills gap. When a significant skills gap occurs in the market, it results in two things. The remuneration demanded by the professionals will sky rocket since there are many chasing the scarce resources. Professionals who are not so skilled will also survive — rather thrive — since lack of alternatives means they will continue to be in demand. ... Security as a public good involves trade-offs with privacy. Whether it is police patrols, or CCTV cameras — a trade-off with privacy is imperative to make security a public good. The privacy trade-off risks will be higher in the cyber world because technology would provide the capability to conduct surveillance at larger scale and also larger depth. It is crucial , delicate — and hence difficult — to strike the right balance between security and privacy such that the extent of privacy sacrificed meets the test of proportionality. However, the complexity of the task, or the associated risks with it, should not prevent us from getting out of the path down a rabbit hole.
Loosely defined, machine data is generated by computers rather than individuals. IoT equipment sensors, cloud infrastructure, security firewalls and websites all throw off a blizzard of machine data that measures machine status, performance and usage. In many cases the same math can analyze machine data for distinct domains, identifying patterns, outliers, etc. Enterprises have well-established processes such as security information and event management (SIEM), and IT operations (ITOps), that process machine data. Security administrators, IT managers and other functional specialists use mature SIEM and ITOps processes on a daily basis. Generally, these architectures perform similar functions as in the first approach, although streaming is a more recent addition. Another difference is that many machine-data architectures have more mature search and index capabilities, as well as tighter integration with business tasks and workflow. Data teams typically need to add the same two functions to complete the CI picture. First, they need to integrate doses of contextual data to achieve similar advantages as those outlined above. Second, they need to trigger business processes, which in this case might mean hooking into robotic process automation tools.
When fintech companies began unbundling, the tools got better but consumers ended up with 15 personal finance apps on their phones. Now, a lot of new fintechs are looking at their offerings and figuring out how to manage all of a person’s personal finances so that other products can be enhanced, said Barnes. “We are not trying to be a bunch of products, but more about how each product helps the other,” Barnes said. “If we offer a checking account, we can see income coming in and be able to give you better access to borrowing. That is the rebuild—how does fintech serve all of the needs, and how do we leverage it for others?” Traditional banking revolves around relationships for which banks can sell many products to maximize lifetime value, said Chris Rothstein, co-founder and CEO of San Francisco-based sales engagement platform Groove, in an interview. Rebundling will become a core part of workflow and a way for fintechs to leverage those relationships to then be able to refer them to other products, he said. “It makes sense long-term,” Rothstein said in an interview. “In financial services, many people don’t want all of these organizations to have their sensitive data. Rebundling will also force incumbents to get better.”
Microsoft’s 5G strategy links the private Azure Edge Zones service it announced earlier this year, Azure IoT Central, virtualized evolved packet core (vEPC) software it gained by acquiring Affirmed Networks, and cloud-native network functions it brought onboard when it acquired Metaswitch Networks. Combining those services under a broader portfolio allows Microsoft to “deliver virtualized and/or containerized network functions as a service on top of a cloud platform that meets the operators where they are, in a model that is accretive to their business,” Hakl said. “We want to harness the power of the Azure ecosystem, which means the developer ecosystem, to help [operators] monetize network slicing, IoT, network APIs … [and] use the power of the cloud” to create the same type of elastic and scalable architecture that many enterprises rely on today, he explained. That vision is split into two parts: the Azure Edge Zones, which effectively extends the cloud to a private edge environment, and the various pieces of software that Microsoft has assembled for network operators. On the latter, Hakl said Microsoft “could have gone out and had our customers teach us that over time. Instead, we acquired two companies that brought in hundreds of engineers that have telco DNA and understand the space.”
Among the various ML solutions, Deep Neural Networks (DNNs) are nowadays considered as the state-of-the-art solution for many problems, including tasks on brain images. Such human brain-inspired algorithms have been proven to be capable of extracting highly meaningful statistical patterns from large-scale and high-dimensional datasets. A DNN is a DL algorithm aiming to approximate some function f ∗. For example, a classifier can be seen as a function y = f * ( x , θ ) mapping a given input x to a category labeled as y. θ is the vector of parameters that the model learns in order to make the best approximation of f ∗. Artificial Neural Networks (ANNs) are built out of a densely interconnected set of simple units, where each unit takes a number of real-valued inputs (possibly the outputs of other units) and produces a single real-valued output (which may become the input to many other units). DNNs are called networks because they are typically represented by composing together many functions. The overall length of the chain gives the depth of the model; from this terminology, the name “deep learning” arises.
A BCI is a system that provides a direct connection between your brain and an electronic device. Since your brain runs on electrical signals like a computer, it could control electronics if you could connect the two. BCIs attempt to give you that connection. There are two main types of BCI — invasive and non-invasive. Invasive devices, like the Neuarlink chip, require surgery to implant them into your brain. Non-invasive BCIs, as you might’ve guessed, use external gear you wear on your head instead. ... A recent study suggested that brain-computer interface technology and NeuraTech in general could measure worker comfort levels in response to their environment. They could then automatically adjust the lights and temperature to make workers more comfortable and minimize distractions. Since distractions take up an average of 2.1 hours a day, these BCIs could mean considerable productivity boosts. The Department of Defense is developing BCIs for soldiers in the field. They hope these devices could let troops communicate silently or control drones with their minds. As promising as BCIs may be, there are still some lingering concerns with the technology. While the Neuralink chip may be physically safe, it raised a lot of questions about digital security.
A fundamental problem, said Brill is the lack of trust in society today. In bold letters, she declared: "The United States has fallen far behind the rest of the world in privacy protection." I can't imagine it's fallen behind Russia, but how poetic if that was true. Still, Brill really isn't happy with our government: "In total, over 130 countries and jurisdictions have enacted privacy laws. Yet, one country has not done so yet: the United States." Brill worries our isolation isn't too splendid. She mused: "In contrast to the role our country has traditionally played on global issues, the US is not leading, or even participating in, the discussion over common privacy norms." That's like Microsoft not participating in the creation of excellent smartphones. It's not too smart. Brill fears other parts of the world will continue to lead in privacy, while the US continues to lead in inaction and chaos. It sounds like the whole company is mad as hell and isn't going to take it anymore. Yet it's not as if Microsoft has truly spent the last 20 years championing privacy much more than most other big tech companies. In common with its west coast brethren, it's been too busy making money.
Quote for the day:
"Leadership is about carrying on when everyone else has given up" -- Gordon Tredgold