Daily Tech Digest - December 06, 2023

Three Ways Generative AI Is Overhauling Data Management

First, prioritize accuracy in SQL generation. NL2SQL has come a long way in understanding natural language queries, but some large language models (LLMs) are better than others in dealing with nuanced or complex questions. Second, ensuring efficient query execution on ad hoc questions is paramount. Historically, interactive querying in a data warehouse environment meant gathering requirements in advance and engineering the data through caching, denormalizing, and other techniques. Generative AI has changed expectations -- users now want immediate answers to novel questions. ... The shift towards vector embeddings is driven by the realization of the remarkable benefits they bring to storing and searching both structured and unstructured data as vectors. The core advantage of vector embeddings lies in their ability to represent complex data in an efficient format. By converting data into high-dimensional vectors, it becomes possible to capture the semantic relationships, context, and similarities between different data points.

Reinforcement learning is useful in environments where precise reward functions can guide the learning process. It’s particularly effective in optimal control scenarios, gaming and aligning large language models (LLMs) with human preferences, where the goals and rewards are clearly defined. Robotics problems, with their complex objectives and the absence of explicit reward signals, pose a significant challenge for traditional RL methods. ... Despite its advantages, imitation learning is not without its pitfalls. A notable issue is the “distribution mismatch problem,” where an agent may encounter situations outside the scope of its training demonstrations, leading to a decline in performance. “Interactive imitation learning” mitigates this problem by having experts provide real-time feedback to refine the agent’s behavior after training. This method involves a human expert monitoring the agent’s policy in action and stepping in with corrective demonstrations whenever the agent strays from the desired behavior.

Don’t make Apache Kafka your database

The right strategy is to let Kafka do what it does best, namely ingest and distribute your events in a fast and reliable way. For example, consider an ecommerce website with an API that would traditionally save all data directly to a relational database with massive tables—with poor performance, scalability, and availability as the result. Introducing Kafka, we can design a superior event-driven ecosystem and instead push that data from the API to Kafka as events. This event-driven approach separates processing into separate components. One event might consist of customer data, another may have order data, and so on—enabling multiple jobs to process events simultaneously and independently. This approach is the next evolution in enterprise architecture. We’ve gone from monolith to microservices and now event-driven architecture, which reaps many of the same benefits of microservices with higher availability and more speed. Once events are sitting in Kafka, you have tremendous flexibility in what you do with them. If it makes sense for the raw events to be stored in a relational databases, use an ecosystem tool like Kafka Connect to make that easy.

What it Takes to Be Your Organisation’s DPO or Data Privacy Lead

Just because you sought expert opinion on the matter a few years ago doesn’t mean you’re in the clear. ‘Once compliant’ doesn’t mean ‘still compliant’. It’s possible that you now need to appoint a DPO (data protection officer) or data privacy lead to be the single point of contact for questions, concerns, breaches, impact assessments or communication with the regulatory authorities. ... It’s not just the EU GDPR, the UK GDPR and the DPA 2018 that we may need to ensure compliance with. Privacy laws exist in almost every country and are relevant wherever you do business. You can design your data privacy systems such that they meet all these legal requirements. ... A DPO isn’t just a trusted adviser during business-as-usual times. They are at the command centre of a cross-functional team in tough times. Faced with an incident or a breach, a well-trained DPO can avert a crisis before social media can cause a catastrophe. Well-versed data privacy leads and DPOs can leap into action when needed, swiftly addressing and remediating issues, reporting to the necessary authorities and instigating lasting change. 

What should be in a company-wide policy on low-code/no-code development

The lesson here is to be thorough in assessment and then document and define existing use cases. Understanding why certain user groups are currently leveraging a particular low-code/no-code platform will help security and business leaders make risk calculations that will determine the course of future policies. The most immediate policy that will come out of this work will be one that defines acceptable use cases for low-code/no-code across the business. “Specify the application of low-code and no-code development across departments, as well as clearly state the purpose of low-code and no-code development,” says Vikas Kaushik, CEO of TechAhead, a development consultancy. This policy of purpose and scope is crucial for setting the course and the tone of the risk management policies around use cases. Some companies may choose to be very granular about this, breaking it down by lines of business, business function, user groups, or teams. Others may simply just delineate between professional developers and so-called citizen developers — tech-savvy business stakeholders.

The Grim Reality of a Cyberattack: From Probability to Certainty

In the unfortunate event that an organization gets hacked, there are certain actions a cybersecurity team can take (or avoid) that significantly impact recovery time and cost. The first action is to report the incident to all relevant authorities, just as someone would declare a physical crime. Many organizations are legally obligated to report such instances and informing the authorities helps protect other enterprises from similar attacks. It is worth noting that authorities and regulators are not going to assign blame. They seek to learn valuable lessons from attacks and build hacker profiles that help minimize the consequences other organizations may face. In addition, enterprises should alert their cyberinsurance providers. This is often a prerequisite for filing a claim, and evidence must be presented to receive compensation. After the appropriate authorities have been notified, it is important that IT teams slow down and avoid making costly mistakes in haste.

AI revolutionising leadership talent identification

With leadership positions being critical for growth and sustenance, any bias in selecting C-suite people can have a damaging impact on the performance of organizations. AI ensures that the entire process of recruiting leaders remains objective, fair, and just. Unlike humans who are driven by feelings and emotions, AI algorithms solely select the candidates based on their skills, competencies, and qualifications. This leaves little room for any prejudice and allows firms to recruit diversified, dynamic, and vibrant individuals at the top echelons of the organization. ... Not only does AI help in recruiting top-level employees but also helps in predicting their engagement behaviours, attrition patterns, and potential switchover. By combining the employment records of candidates with their present level of engagement, AI can predict the attrition rate at the top leadership positions. For example, AI tools can alert employers about the sudden decrease in employees’ engagement levels or increase in their job search activity online. The information can be used by employers to enhance engagement with their employees, enhance their talent retention efforts, or devise a contingency plan in case of a sudden exit.

If You Want People to Follow You, Stop Being a Boss — 8 Steps to Truly Effective Leadership

The approach to mistakes and failures differentiates a leader from a boss. Where a boss might see a mistake as a failure to be criticized, a leader views it as an opportunity for growth. Positive reinforcement involves recognizing the effort, providing constructive feedback, and encouraging a mindset of continuous learning. This approach not only helps in skill development but also instills a sense of confidence and loyalty within the team, fostering a workplace culture where innovation is encouraged, and risks are viewed as steps towards growth. ... Empowerment is a key trait of effective leadership. It involves trusting the team's capabilities and allowing autonomy in their roles. This empowerment fosters a sense of ownership and responsibility among team members, leading to greater job satisfaction and innovation. In contrast, micromanagement can stifle creativity, lower morale and hinder productivity. Leaders who empower rather than micromanage find their teams are more motivated, creative, and ultimately more effective in achieving organizational goals.

Data governance and government: The need for effective and protective data management

Data governance in government involves establishing and enforcing policies, procedures, and standards to ensure the effective management, use, and protection of data. Several key issues and challenges are commonly faced in the context of data governance in government:Data privacy and security: Governments handle vast amounts of sensitive and personally identifiable information. Ensuring the privacy and security of this data is a paramount concern, especially in the face of increasing cyber threats and data breaches. Compliance with regulations: Governments must adhere to various regulations and compliance standards concerning data management, such as data protection laws, privacy regulations, and industry-specific requirements. Interoperability: Government agencies often operate with disparate systems and databases. Achieving interoperability and ensuring seamless data exchange among different agencies is a significant challenge impacting the efficiency and effectiveness of government services. 

Linus Torvalds on the state of Linux today and how AI figures in its future

Indeed, Torvalds hopes that AI might really help by being able "to find the obvious stupid bugs because a lot of the bugs I see are not subtle bugs. Many of them are just stupid bugs, and you don't need any kind of higher intelligence to find them. But having tools that warn more subtle cases where, for example, it may just say 'this pattern does not look like the regular pattern. Are you sure this is what you need?' And the answer may be 'No, that was not at all what I meant. You found an obvious bag. Thank you very much.' We actually need autocorrects on steroids. I see AI as a tool that can help us be better at what we do." But, "What about hallucinations?," asked Hohndel. Torvalds, who will never stop being a little snarky, said, "I see the bugs that happen without AI every day. So that's why I'm not so worried. I think we're doing just fine at making mistakes on our own." Moving on, Torvalds said, "I enjoy the fact that open source, the notion of openness, has gotten so much more widely accepted. I enjoyed it particularly because I remember what it was thirty years ago when I had started this project, and people would ask me, 'Why?'

Quote for the day:

"To be successful you must accept all challenges that come your way. You can't just accept the ones you like." -- Mike Gafka

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