Machine learning algorithms relentlessly search for a solution. In the case of GANs, the generator and discriminator network somehow finds a way to fool each other. The result is a Deepfake. Not that deep fakes are harmless but ML is used in more critical industries such as healthcare. So when a model that is fed with an underrepresented dataset is used, the chances of misdiagnosis increases. “Each ML algorithm has a strategy to answer optimally to your question,” warned Luca. ... The different definitions makes things even more cumbersome for the data scientist. Citing the work on the impossibility of fairness, Luca also explained why some notions of fairness are mutually incompatible and cannot be satisfied simultaneously. “ There is no single universal metric for quantifying fairness that can be applied to all ML problems,” he added. No matter how fool proof the data curation process is, loopholes might creep in. So, what are these loopholes? ... When it comes to ML fairness toolkits, Google’s TensorFlow team has been on the top. The team has been developing multiple tools to assist niche areas within the realms of fairness debate. The whole debate around ML fairness is forcing companies like Google to establish an ecosystem of fairer ML practice through their tools.
There are already some great editors, but nothing of the calibre of VS Code. I can take my $35 computer, plug it into a keyboard and mouse, connect a monitor and a TV and code in a wide range of languages from the same place. I see kids learning Python at school using one tool, then learning web development in an after-school coding club with a different tool. They can now do both in the same application, reducing the cognitive load – they only have to learn one tool, one debugger, one setup. Combine this with the new Raspberry Pi 400 and you have an all-in-one solution to learning to code, reminiscent of my ZX Spectrum of decades ago, but so much more powerful. The second reason is to me the most important — it allows kids to share the same development environment as their grown-ups. Imagine the joy of a 10-year-old coding Python using VS Code on their Raspberry Pi plugged into the family TV, then seeing their Mum working from home coding Python in exactly the same tool on her work laptop as part of her job as an AI engineer or data scientist. It also makes it easier when Mum has to inevitably help with unblocking the issues that always come up with learners.
While Soda SQL is more geared toward data engineers, Soda also offers a hosted service geared toward the business user and, specifically, the chief data officer (CDO). Interest in data testing and monitoring might start with the CDO when they recognize the need to ensure quality data feeding executive dashboards, machine learning models, and more. At the same time, data engineers, responsible for building data pipelines (transforming, extracting, and preparing data for usage), just need to do some minimal checks to ensure they're not shipping faulty data. Or, you might have a data platform engineer who just wants hands-off monitoring after connecting to the data platform warehouse. In this universe, data testing and data monitoring are two distinct things. In both cases, Baeyens said, "The large majority of people with which we speak have an uncomfortable feeling that they should be doing more with data validation, data testing, and monitoring, but they don't know where to start, or it's just kind of blurry for them." Soda is trying to democratize data monitoring, in particular, by making it easy for non-technical, business-oriented people to build the data monitors.
We see lots of incidents, but there’s no obligation for the owners and operators to disclose the incident. The incidents that you see in the media are often just a small percentage of the incidents that you actually see in the public eye. We know of many serious incidents that you’ll never read in the headlines and for good reason, really. So, what I would do is say that cybersecurity is still a priority for many organizations. It’s their number one risk, and it’s something that they’re dealing with every day. ... Ask the question, “What is the problem that I’d like to solve, as a result of implementing digital where any other solution couldn’t?” If you’re already on that journey, I would be looking back and reviewing and saying, “Does my digital solution so far answer the question? Is it solving the problem that I want to solve as a result of a digital solution?” In a recent study, we found that less than 20% of organizations have more than a third of the employees actually trained in digital, and trained in their digital strategy as an organization. But, more than 60% of our customers actually have a digital strategy, so there’s a mismatch between customers in heading out on the digital journey, but not really taking their employees with them.
While organisations will never have as much control over a supplier’s security as they do their own, they can take steps to minimise risks. Security standards must be set out within service level agreements (SLAs), for instance, insisting that the third-party meets ISO 27001 accreditation as a minimum and ensuring that the supplier has a framework of policies and procedures governing information risk management processes. Unfortunately, this approach is rare. The UK Government’s Data Breaches Survey 2019 indicates that less than one in five businesses (18%) demanded that their suppliers have any form of cybersecurity standard or good practice guidelines in place. The issue also becomes more complicated when the sheer scale and intricacy of the average supply chain network comes into play. A firm may have its data stolen from a company three or four connections deep into the supply chain. If the breached third-party lacks the ability to detect an attack itself, a company’s data could be in the hands of criminals for months before they are finally alerted to the breach. Even if a security breach originates with a third party, it will carry just as much of a financial and reputational cost as a direct attack on the organisation’s own network.
The notion of ethics has evolved. Decisions around right and wrong always depended on human cognition and were guided by popular sentiments and socially acceptable norms. Now, with the rise of AI, machines are slowly taking over human cognition functions, a phenomenon that author Ray Kurzweil predicts will increase over time and culminate in the advent of singularity where machines irrevocably take over humans, possibly at some distant point in the future. This trend is causing technologists, researchers, policymakers and society at large to rethink how we interpret and implement ethics in the age of AI. ... To face the challenges of the future, we also need to develop a new discipline of meta-intelligence by taking inspiration from the concepts of metadata and metaethics. Doing so will help us improve the traceability and trustworthiness of AI-driven insights. The concept of meta-intelligence has been doing the rounds of thought leadership for the last few years, especially led by people thinking about and working on singularity. The pace of technological evolution and the rise of AI has become essential for human progress today. Businesses around the world are getting impacted by the transformative power of these technologies.
With the X65, unveiled Tuesday, users will get a bump in speed but also see better battery life. Coverage will improve, latency will decrease and applications will be even more responsive than they are with Qualcomm's earlier X60 modem technology. And capacity will be "massive," letting more people on a network make reliable and crisp video calls with their doctors and face off against rivals in streaming games. With the previous-generation X60 modem, just now arriving in smartphones like Samsung's Galaxy S21, you can download data over 5G networks at up to 7.5Gbps and upload information as fast as 3Gbps, only slightly faster than the previous generation of modem. But the X60 also has the ability to aggregate the slower but more reliable sub-6 networks with the faster but finicky millimeter-wave spectrum, boosting overall performance and helping users see faster average speeds. The X65 has the same benefit. While it's unlikely that you'll regularly -- or maybe even ever -- see 10Gbps download speeds, you'll consistently see speeds that are magnitudes faster than your current 4G smartphone.
NGINX is a high-performance HTTP server as well as a reverse proxy. Unlike traditional servers, NGINX follows an event-driven, asynchronous architecture. As a result, the memory footprint is low and performance is high. If you’re running a Node.js-based web app or .NET Core Web Application, you should seriously consider using NGINX as a reverse proxy. NGINX can be very efficient in serving static assets as well. For all other requests, it will talk to your Node.js back end or .NET Core Web Application and send the response to the client. ... Although the focus of this article is NGINX. But we will be dealing with a little bit of bash commands, NodeJS, and .NET Core. I have written about all of these topics on DZone, so you check my other articles for background information on these topics if needed. ... A reverse proxy server is a web server that accepts requests and sends them to another web server which actually creates the responses for those requests. The responses are sent back to the proxy server who forwards them to the clients who issued the corresponding requests. Nginx is a web server that can act as a reverse proxy for ASP.NET Core applications and which is also very good at serving static content.
AI cannot yet plan and does not have a purpose. Students need to hone skills in purposeful writing that achieves their communication goals. Unfortunately, the NAPLAN regime has hampered teaching writing as a process that involves planning and editing. This is because it favours time-limited exam-style writing for no audience. Students need to practise writing in which they are invested, that they care about and that they hope will effect change in the world as well as in their genuine, known readers. This is what machines cannot do. AI is not yet as complex as the human brain. Humans detect humour and satire. They know words can have multiple and subtle meanings. Humans are capable of perception and insight; they can make advanced evaluative judgements about good and bad writing. There are calls for humans to become expert in sophisticated forms of writing and in editing writing created by robots as vital future skills. Nor does AI have a moral compass. It does not care. OpenAI’s managers originally refused to release GPT-3, ostensibly because they were concerned about the generator being used to create fake material, such as reviews of products or election-related commentary.
A top-down approach to building data and analytics platforms, based on data governance best practices and policies, is often the choice. This approach can provide a cohesive and robust solution that complies well with privacy regulations, and where all the components interact well, adhering to strict security policies. Unfortunately, it can often become cumbersome for users and slow the time-to-value, with data consumers forced to adapt their data usage and consumption to the strict compliance and security-driven protocols driving the platform. On the flip side, a bottom-up approach to data analytics is engineering and design-focused, with the goal of introducing incremental deliverables that add value to the platform in response to the user’s needs. ... Whether top-down or bottom-up, it’s critical for organizations to start with documenting privacy, security, data risks, controls, and technology needs around data access to address topics like culture of federated data ownership, adoption of self-service or collaboration across teams around critical data sets, and enterprise-wide technology standards for certain key areas.
Quote for the day:
“Believe in your infinite potential. Your only limitations are those you set upon yourself.” -- Roy T. Bennett