Daily Tech Digest - August 30, 2023

Generative AI Faces an Existential IP Reckoning of Its Own Making

Clearly, this situation is untenable, with a raft of dire consequences already beginning to emerge. Should the courts determine that generative AI firms aren’t protected by the fair use doctrine, the still-budding industry could be on the hook for practically limitless damages. Meanwhile, platforms like Reddit are beginning to aggressively push back against unchecked data scraping. ... These sorts of unintended externalities will only continue to multiply unless strong measures are taken to protect copyright holders. Government can play an important role here by introducing new legislation to bring IP laws into the 21st century, replacing outdated regulatory frameworks created decades before anyone could have predicted the rise of generative AI. Government can also spur the creation of a centralized licensing body to work with national and international rights organizations to ensure that artists, content creators, and publishers are being fairly compensated for the use of their content by generative AI companies.

6 hidden dangers of low code

The low-code sales pitch is that computers and automation make humans smarter by providing a computational lever that multiplies our intelligence. Perhaps. But you might also notice that, as people grow to trust in machines, we sometimes stop thinking for ourselves. If the algorithm says it’s the right thing to do, we'll just go along with it. There are endless examples of the disaster that can ensue from such thoughtlessness. ... When humans write code, we naturally do the least amount of work required, which is surprisingly efficient. We're not cutting corners; we're just not implementing unnecessary features. Low code solutions don’t have that advantage. They are designed to be one-size-fits-all, which in computer code means libraries filled with endless if-then-else statements testing for every contingency in the network. Low code is naturally less efficient because it’s always testing and retesting itself. This ability to adjust automatically is the magic that the sales team is selling, after all. But it’s also going to be that much less efficient than hand-tuned code written by someone who knows the business.

Applying Reliability Engineering to the Manufacturing IT Environment

To understand exposure to failure, the Reliability Engineers analyzed common failure modes across manufacturing operations, utilizing the Failure Mode and Effects Analysis (FMEA) methodology to anticipate potential issues and failures. Examples of common failure modes include “database purger/archiving failures leading to performance impact” and “inadequate margin to tolerate typical hardware outages.” The Reliability Engineers also identified systems that were most likely to cause factory impact due to risk from these shared failure modes. This data helped inform a Resiliency Maturity Model (RMM), which scores each common failure mode on a scale from 1 to 5 based on a system’s resilience to that failure mode. This structured approach enabled us to not just fix isolated examples of applications that were causing the most problems, but to instead broaden our impact and develop a reliability mindset. 

5 Skills All Marketing Analytics and Data Science Pros Need Today

Marketing analysts should hone their skills to know who to talk to – and how to talk to them – to secure the information they have. Trust Insights’ Katie Robbert says it requires listening and asking questions to understand what they know that you need to take back to your team, audience, and stakeholders. “You can teach anyone technical skills. People can follow the standard operating procedure,” she says. “The skill set that is so hard to teach is communication and listening.” ... By improving your communication skills, you’ll be well-positioned to follow Hou’s advice: “Weave a clear story in terms of how marketing data could and should guide the organization’s marketing team.” She says you should tell a narrative that connects the dots, explains the how and where of a return on investment, and details actions possible not yet realized due to limited lines of sight. ... Securing organization-wide support requires leaning into what the data can do for the business. “Businesspeople want to see the business outcomes. 

Neural Networks vs. Deep Learning

Neural networks, while powerful in synthesizing AI algorithms, typically require less resources. In contrast, as deep learning platforms take time to get trained on complex data sets to be able to analyze them and provide rapid results, they typically take far longer to develop, set up and get to the point where they yield accurate results. ... Neural networks are trained on data as a way of learning and improving their conclusions over time. As with all AI deployments, the more data it’s trained on the better. Neural networks must be fine-tuned for accuracy over and over as part of the learning process to transform them into powerful artificial intelligence tools. Fortunately for many businesses, plenty of neural networks have been trained for years – far before the current craze inspired by ChatGPT – and are now powerful business tools. ... Deep learning systems make use of complex machine learning techniques and can be considered a subset of machine learning. But in keeping with the multi-layered architecture of deep learning, these machine learning instances can be of various types and various strategies throughout a single deep learning application.

Ready or not, IoT is transforming your world

At its core, IoT refers to the interconnection of everyday objects, devices, and systems through the internet, enabling them to collect, exchange, and analyze data. This connectivity empowers us to monitor and control various aspects of our lives remotely, from smart homes and wearable devices to industrial machinery and city infrastructure. The essence of IoT lies in the seamless communication between objects, humans, and applications, making our environments smarter, more efficient, and ultimately, more convenient. ... Looking ahead, the future of IoT holds remarkable potential. Over the next five years, we can expect a multitude of advancements that will reshape industries and lifestyles. Smart cities will continue to evolve, leveraging IoT to enhance sustainability, security, and quality of life. The healthcare sector will witness even more personalized and remote patient monitoring, revolutionizing the way medical care is delivered. AI and automation will play a pivotal role, in driving efficiency and innovation across various domains.

What are network assurance tools and why are they important?

Without a network assurance tool at their disposal, many enterprises would be forced to limit their network reach and capacity. "They would be unable to take advantage of the latest technological advancements and innovations because they didn’t have the manpower or tools to manage them," says Christian Gilby, senior product director, AI-driven enterprise, at Juniper Networks. "At the same time, enterprises would be left behind by their competitors because they would still be utilizing manual, trial-and-error procedures to uncover and repair service issues." The popularity of network assurance technology is also being driven by a growing enterprise demand for network teams to do more with less. "Efficiency is needed in order to manage the ever-expanding network landscape," adds Gilby. New devices and equipment are constantly brought online and added to networks. Yet enterprises don’t have unlimited IT budgets, meaning that staffing levels often remain the same, even as workloads increase.

How tomorrow’s ‘smart cities’ will think for themselves

In the smart cities of the future, technology will be built to respond to human needs. Sustainability is the biggest problem facing cities – and by far the biggest contributor is the automobile. Smart cities will enable the move towards reducing traffic, and towards autonomous vehicles directed efficiently through the streets. Deliveries which are not successful the first time are one example. These are a key driver of congestion, as drivers have to return to the same address repeatedly. In a cognitive city, location data that shows when a customer is home can be shared anonymously with delivery companies – with their consent – so that more deliveries arrive on the first attempt. Smart parking will be another important way to reduce congestion and make the streets more efficient. Edge computing nodes will sense empty parking spaces and direct cars there in real-time. They will also be a key enabler for autonomous driving, delivering more data points to autonomous systems in cars. 

Navigating Your Path to a Career in Cyber Security: Practical Steps and Insights

Practical experience is critical in the field of cyber security. Seek opportunities to apply your knowledge and gain hands-on experience as often as you can. I recommend looking for internships, part-time jobs, or volunteer positions that allow you to work on real-world projects and develop practical skills. I cannot stress how important it is to understand the fundamentals. ... Networking is essential for finding job opportunities in any field, including cybersecurity. You should attend industry events and conferences (there are plenty of free ones) and try to meet as many professionals already working in the field as possible. Their insights will go a long way in your journey to finding the right role. There are also many online communities and forums you can join where cyber security experts gather to discuss trends, share knowledge, and explore job opportunities. Networking will help you gain insights, discover job openings, and even receive recommendations from industry professionals.

NCSC warns over possible AI prompt injection attacks

Complex as this may seem, some early developers of LLM-products have already seen attempted prompt injection attacks against their applications, albeit generally these have been either rather silly or basically harmless. Research is continuing into prompt injection attacks, said the NCSC, but there are now concerns that the problem may be something that is simply inherent to LLMs. This said, some researchers are working on potential mitigations, and there are some things that can be done to make prompt injection a tougher proposition. Probably one of the most important steps developers can take is to ensure they are architecting the system and its data flows so that they are happy with the worst-case scenario of what the LLM-powered app is allowed to do. “The emergence of LLMs is undoubtedly a very exciting time in technology. This new idea has landed – almost completely unexpectedly – and a lot of people and organisations (including the NCSC) want to explore and benefit from it,” wrote the NCSC team.

Quote for the day:

"When you practice leadership, the evidence of quality of your leadership, is known from the type of leaders that emerge out of your leadership" -- Sujit Lalwani

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