The data science community’s tendency to aim for data-“insatiable” and compute-draining state-of-the-art models in certain domains (e.g. the NLP domain and its dominant large-scale language models) should serve as a warning sign. OpenAI analyses suggest that the data science community is more efficient at achieving goals that have already been obtained but demonstrate that it requires more compute, by a few orders of magnitude, to reach new dramatic AI achievements. MIT researchers estimated that “three years of algorithmic improvement is equivalent to a 10 times increase in computing power.” Furthermore, creating an adequate AI model that will withstand concept-drifts over time and overcome “underspecification” usually requires multiple rounds of training and tuning, which means even more compute resources. If pushing the AI envelope means consuming even more specialized resources at greater costs, then, yes, the leading tech giants will keep paying the price to stay in the lead, but most academic institutions would find it difficult to take part in this “high risk – high reward” competition. These institutions will most likely either embrace resource-efficient technologies or persue adjacent fields of research.
By far, the biggest threat that homeowners face concerning all of their connected devices is the chance that an outsider might gain access to them and use them for nefarious purposes. The recent past is littered with examples of such devices becoming part of sophisticated botnets that end up taking part in massive denial of service attacks. But although you wouldn’t want any of your devices used for such a purpose, the truth is that if it happened, it likely wouldn’t affect you at all (not that I’m advocating that anyone ignore the threat). The average person really should be worried about the chance that a hacker might use the access they gain to a connected device as a jumping-off point to a larger breach of the network. That exact scenario has already played out inside multiple corporate networks, and the same is possible for in-home networks as well. And if it happens, a hacker might gain access to the data stored on every PC, laptop, tablet, and phone connected to the same network as the compromised device. And that’s what the following plan should help to prevent. In any network security strategy, the most important tool available in isolation. That is to say; the goal is to wall off access between the devices on your network so that a single compromised device can’t be used as a means of getting at anywhere else.
First, you need to overcome the technical skills barrier. For that you need the right people. There is a difference in developing hardware or software as much as selling a one-time sales product or a service with recurring fees. Yes, you can train people to a certain extent to do so. But what we’ve realised at Halma is that diversity, equality and inclusion are just as important to digital & innovation success as every other aspect of business performance. At Halma this approach to diversity is in our DNA. Attracting and recruiting people with diverse viewpoints as well as diverse skills, mean that you will be able to see new opportunities and imagine new solutions. Second, you need to overcome the business model barrier. You need to think differently about how your business generates revenue. Fixed mindsets in your team that don’t have an outside-in approach to your market and are hooked on business as usual need to be changed. You need to take a bold and visionary approach to doing business differently, and helping your team reimagine their old business model. Third, you need to overcome the business structure barrier. Often the biggest barrier to cultural adaptation is the organisation itself. Using the same tools and strategies that built your business today isn’t going to enable the digital transformation of tomorrow. It requires a fundamental shift in the way your organisation works.
“If there’s a thing that, as a security person, you’d call a ‘vulnerability,’ keep that word to yourself and instead speak the language of the developers: it’s a defect,” he pointed out. “Developers are already incentivized to manage defects in code. Allow those existing prioritization and incentivization tools to do their job and you’ll gain the security-positive outcomes that you’re looking for.” ... “Organizations need to stop treating security as some kind of special thing. We used to talk about how security was a non-functional requirement. Turns out that this was a wrong assumption, because security is very much a function of modern software. This means it needs to be included as you would any other requirement and let the normal methods of development defect management take over and do what they already do,” he noted. “There will be some uplift requirements to ensure your development staff understands how to write tests that validate security posture (i.e., a set of tests that exercise your user input validation module), but this is generally not a significant problem as long as you’ve built in the time to do this kind of work by including the security requirements in that set of epics and stories that fit within the team’s sprint budget.”
Machine Learning is at the heart of what makes Azure Sentinel a game-changer in the SOC, especially in terms of alert fatigue reduction. With Azure Sentinel we are focusing on three machine learning pillars: Fusion, Built-in Machine Learning, and “Bring your own machine learning.” Our Fusion technology uses state-of-the-art scalable learning algorithms to correlate millions of lower fidelity anomalous activities into tens of high fidelity incidents. With Fusion, Azure Sentinel can automatically detect multistage attacks by identifying combinations of anomalous behaviors and suspicious activities that are observed at various stages of the kill-chain. On the basis of these discoveries, Azure Sentinel generates incidents that would otherwise be difficult to catch. Secondly, with built-in machine learning, we pair years of experience securing Microsoft and other large enterprises with advanced capabilities around techniques such as transferred learning to bring machine learning to the reach of our customers, allowing them to quickly identify threats that would be difficult to find using traditional methods. Thirdly, for organizations with in-house capabilities to build machine learning models, we allow them to bring those into Azure Sentinel to achieve the same end-goal of alert noise reduction in the SOC.
Jack of all trades doesn’t cut it anymore. While data science has many applications, people will pay more bucks if you are an expert at one thing. For instance, your value as a data scientist will be worth its weight in gold if you are exceptional at data visualisations in a particular language rather than a bits and pieces player. The top technical skills in demand in 2021 are data wrangling, machine learning, data visualisation, analytics tools, etc. As a data scientist, it’s imperative to know your fundamentals down cold. It would help if you spent enough time with your data to extract actionable insights. A data scientist should sharpen her skills by exploring, plotting and visualising data as much as possible. Most data scientists or aspiring data scientists doing statistics learn to code or take up a few machine learning or statistics classes. However, it is one thing to code little models on practice platforms and another thing to build a robust machine learning project deployable in the real world. As a rule, data scientists need to learn the fundamentals of software engineering and real-world machine learning tools.
Matthew Hodgson, CEO and founder of Mosaic Smart Data, says AI and automation is “permeating virtually every corner of capital markets.” He believes that this technology will form the keystone of the future of business intelligence for banks and other financial institutions. The capabilities and potential of AI are enormous for our industry. According to Hodgson, recent studies have found that companies not using AI are likely to suffer in terms of revenue. “As the link between AI use and revenue growth continues to strengthen, there can be no doubt that AI will be a driving force for the capital markets in 2021 and in the decade ahead — those firms who are unwilling to embrace it are unlikely to survive,” he continues. Hodgson predicts that with the continued tightening regulatory environment, financial institutions will have to do more with less and many will need to act fast to remain both competitive and relevant in this ‘new normal’. “As a result, we are seeing that financial institutions are increasingly looking to purchase out-of-the-box third-party solutions that can be onboarded within a few short months and that deliver immediate results rather than taking years to build their own systems with the associated risks and vast hidden costs,” he adds.
In the first pass, I scan the abstract to understand if the paper has what I need. If it does, I skim through the headings to identify the problem statement, methods, and results. In this example, I’m specifically looking for formula on how to calculate the various metrics. I give all papers on my list a first pass (and resist starting on a second pass until I’ve completed the list). In this example, about half of the papers made it to the second pass. In the second pass, I go over each paper again and highlight the relevant sections. This helps me quickly spot important portions when I refer to the paper later. Then, I take notes for each paper. In this example, the notes were mostly around metrics (i.e., methods, formula). If it was a literature review for an application (e.g., recsys, product classification, fraud detection), the notes would focus on the methods, system design, and results. ... In the third pass, I synthesize the common concepts across papers into their own notes. Various papers have their own methods to measure novelty, diversity, serendipity, etc. I consolidate them into a single note and compare their pros and cons. While doing this, I often find gaps in my notes and knowledge and have to revisit the original paper.
While Gen Zers and Millennials are coming into their own in the workforce, Baby Boomers are leaving in droves, taking valuable expertise and experience with them that’s often not documented throughout the organization. Pew Research reports 3.3 million people retired in the third quarter of 2020 -- likely driven by staff reductions and incentivized retirement packages created by the pandemic. The change in rank will inevitably drive how people interact with technology, particularly around the transfer of knowledge to bridge the skills gap. While this transition is still in flux, we’ve already been able to imagine the impact. Coding languages risk becoming extinct, and machinery risks grinding to a halt. Data from recruitment firm Robert Half reveals three quarters of finance directors believe the skills gap created by retiring Baby Boomers will negatively impact their business within 2-5 years. To that point, the COVID pandemic is not only creating turnover in the workforce but is also making in-person knowledge sharing difficult. Technology is helping to soften this challenge, ensuring business resiliency against the “disruption” of retirement. Where practical knowledge handovers are less viable, in the case of remote work or global organizations, programming languages or process-specific knowledge can be taught through artificial intelligence (AI).
The concept of active/active architectures is not a new one and can in fact be traced back to the 70s when digital database systems were being newly introduced in the public sphere. Now as cloud vendors roll out new services, one of the factors they are abstracting away for users is the set-up of such a system. After all, one of the major promises of moving to the cloud is the abstraction of these types of complexities along with the promise of reliability. Today, an effective active/active multi-region architecture can be built on almost all cloud vendors out there. Considering the ability and maturity of cloud services in the market today, this article will not act as a tutorial on how to build the intended architecture. There are already various workshop guides and talks on the matter. In fact, one of the champions of resilient and high available cloud architectures, Adrian Hornsby who is the Principal Technical Evangelist at AWS, has a great series of blogs guiding the reader through active/active multi-region architectures on AWS. However, what is missing, or at least what has been lost, is the theory and clear understanding of the motive behind implementing such an architecture.
Quote for the day:
"Expression is saying what you wish to say, Impression is saying what others wish to listen." -- Krishna Sagar