Applying Technology Laws

Huang's Law  and Moore's Law are technology "laws." Maybe it is more accurate to say they are observations, but "law" has become attached to these observations since they appear to remain true.

Moore's law is the observation that the number of transistors in an integrated circuit (IC) doubles about every two years. Moore's law is an observation and projection of a historical trend. Rather than a law of physics, it is an empirical relationship linked to gains from experience in production.

Gordon Moore, the co-founder of Fairchild Semiconductor and Intel (and former CEO of the latter), posited in 1965 posited the idea and projected this rate of growth would continue for at least another decade. In 1975, looking forward to the next decade, he revised the forecast to doubling every two years. His prediction has held since 1975 and has since become known as a "law".

Moore's prediction has been used in the semiconductor industry to guide long-term planning and to set targets for research and development, thus functioning to some extent as a self-fulfilling prophecy.

Huang’s Law has been called the new Moore’s Law. It seems that the law that the same dollar buys twice the computing power every 18 months is no longer true.

Huang's law is an observation in computer science and engineering that advancements in graphics processing units (GPUs) are growing at a rate much faster than with traditional central processing units (CPUs). The observation is in contrast to Moore's law as Huang's law states that the performance of GPUs will more than double every two years.

Jensen Huang was then CEO of Nvidia and at the 2018 GPU Technology Conference and observed that Nvidia’s GPUs were "25 times faster than five years ago" whereas Moore's law would have expected only a ten-fold increase. As microchip components became smaller, it became harder for chip advancement to meet the speed of Moore's law.

tech in oppositionHuang's Law and Moore's Law are concepts primarily associated with the semiconductor industry and technology advancements. However, their principles can be extended and applied to various domains beyond technology.

You can extend Huang's Law to other fields where exponential growth or improvement is observed. For example, consider advancements in renewable energy efficiency, healthcare outcomes, or educational achievements. The idea is to identify areas where progress follows an exponential curve and apply the principles accordingly.

Both laws highlight the concept of scaling - either in computational power (Moore's Law) or AI efficiency (Huang's Law). You could apply this principle to other systems and processes where scaling can lead to significant improvements.

I am imagining a discussion (probably in a classroom setting) about ethical considerations, such as the impact of rapid advancements on society, and focus on responsible and ethical development in various fields. That certainly is true currently in discussions of AI.

Begin. End. The Waning Days of Coding

code on screen

A piece in The New Yorker (not exactly a technology magazine) titled "A Coder Considers the Waning Days of the Craft," set me thinking about what tech careers will be lost in the near and far future. Yes, artificial intelligence plays into this, but there are other factors too. Coding seems to be a likely candidate for being on the decline.

The author, James Somers, says that, "Coding has always felt to me like an endlessly deep and rich domain. Now I find myself wanting to write a eulogy for it." With his wife pregnant, he wonders that "...by the time that child can type, coding as a valuable skill might have faded from the world." 

It is an interesting read. Kind of a memoir of a coder.

Schools still teach coding. Coders are still working. The question is for for how long? Should a student in middle school think about it as a career? I used to tell my middle school students that a lot of them will go into careers that have titles that don't exist today. Who can predict?

Somers concludes:

"So maybe the thing to teach isn’t a skill but a spirit. I sometimes think of what I might have been doing had I been born in a different time. The coders of the agrarian days probably futzed with waterwheels and crop varietals; in the Newtonian era, they might have been obsessed with glass, and dyes, and timekeeping. I was reading an oral history of neural networks recently, and it struck me how many of the people interviewed—people born in and around the nineteen-thirties—had played with radios when they were little. Maybe the next cohort will spend their late nights in the guts of the A.I.s their parents once regarded as black boxes. I shouldn’t worry that the era of coding is winding down. Hacking is forever."

The future of coding is likely to be affected by all of these factors:

Artificial Intelligence and Automation: AI is already influencing coding through tools that assist developers in writing code, debugging, and optimizing algorithms. As AI continues to advance, it may take on more complex coding tasks, allowing developers to focus on higher-level design and problem-solving.

Low-Code/No-Code Development: The rise of low-code and no-code platforms is making it easier for individuals with limited programming experience to create applications. This trend could democratize software development, enabling a broader range of people to participate in creating digital solutions.

Increased Specialization: With the growing complexity of technology, developers are likely to become more specialized in particular domains or technologies. This could lead to a more segmented job market, with experts in areas like AI, cybersecurity, blockchain, etc.

Remote Collaboration and Distributed Development: Remote work has become more prevalent, and this trend is likely to continue. Tools and practices for collaborative and distributed development will become increasingly important.

Ethical Coding and Responsible AI: As technology plays a more central role in our lives, the ethical considerations of coding will become more critical. Developers will need to be mindful of the societal impact of their creations and consider ethical principles in their coding practices.

Continuous Learning: The pace of technological change is rapid, and developers will need to embrace a mindset of continuous learning. Staying updated with the latest tools, languages, and methodologies will be crucial.

Quantum Computing: While still in its early stages, quantum computing has the potential to revolutionize certain aspects of coding, particularly in solving complex problems that are currently intractable for classical computers.

Augmented Reality (AR) and Virtual Reality (VR): As AR and VR technologies become more widespread, developers will likely be involved in creating immersive experiences and applications that leverage these technologies.

Cybersecurity Emphasis: With the increasing frequency and sophistication of cyber threats, coding with a focus on security will be paramount. Developers will need to incorporate secure coding practices and stay vigilant against emerging threats.

Environmental Sustainability: As concerns about climate change grow, there may be a greater emphasis on sustainable coding practices, including optimizing code for energy efficiency and reducing the environmental impact of data centers.

How do I know this? Because I asked a chatbot to tell me the future of coding.

Micromobility

scootersMicromobility refers to a range of small, lightweight vehicles operating at speeds typically below 25 km/h (15 mph) and driven by users personally. Micromobility devices include bicycles, e-bikes, electric scooters, electric skateboards, shared bicycle fleets, and electric pedal-assisted (pedelec) bicycles, and even hoverboards. The term "micromobility" was originally coined by Horace Dediu in 2017.

There are benefits and challenges for individuals using this type of transportation, including lower initial cost, maintenance, fuel, and parking costs in many instances. The cost for some of those options can even be zero, as with fuel for a traditional bicycle or scooter.

There are also benefits and concerns for communities. Particularly in the ever-evolving landscape of urban transportation, micromobility solutions are revolutionizing the way people commute, reducing congestion, improving air quality and redefining urban mobility.

Some of these modes are electric, some are traditional, such as bicycles, scooters, and skateboards, and some are hybrid. The range of micromobility vehicles offers compact, eco-friendly and convenient options for short-distance travel. By reducing congestion, decreasing carbon emissions and promoting active lifestyles, micromobility has the potential to positively impact urban environments.

One safety concern, particularly in urban areas, is how these vehicles will interact with pedestrains and traditional larger vehicles on roadways.

For more on this topic, see transportation.gov/rural/electric-vehicles/ev-toolkit/electric-micromobility

 

micromobility group

AI and Bias

Bias has always existed. It has always existed online. Now, with AI, there is another level of bias.

Bias generated by technology is “more than a glitch,” says one expert.

For example, why does AI have a bias against dark skin? It is because its data is scraped from the Internet, and the Internet is full of biased content.

This doesn't give AI a pass on bias. It is more of a comment or reflection on bias in general.