June 06, 2024
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
The great thing about AI is that it’s software-upgradable. When you buy an AI phone, the phone gets better mainly through software updates, not hardware updates. ... As we’re talking back and forth with AI agents, people will use earbuds and, increasingly, AI glasses to interact with AI chatbots. The glasses will use built-in cameras for photo and video multimodal AI input. As glasses become the main interface, the user experience will likely improve more with better glasses (not better phones), with improved light engines, speakers, microphones, batteries, lenses, and antennas. With the inevitable and inexorable miniaturization of everything, eventually a new class of AI glasses will emerge that won’t need wireless tethering to a smartphone at all, and will contain all the elements of a smartphone in the glasses themselves. ... Glasses will prove to be the winning device, because glasses can position speakers within an inch of the ears, hands-free microphones within four inches of the mouth and, the best part, screens directly in front of the eyes. Glasses can be worn all day, every day, without anything physically in the ear canal. In fact, roughly 4 billion people already wear glasses every day.
Storage is still cheap. We should really still be thinking about storage as being pretty cheap. Calling APIs costs money. It's always going to cost money. In fact, you should accept that anything you do in the cloud costs money. It might not be a lot; it might be a few pennies. It might be a few fractions of pennies, but it costs money. It would be best to consider that before you call an API. The cloud has given us practically infinite scale, however, I have not yet found an infinite wallet. We have a system design constraint that no one seems to be focusing on during design, development, and deployment. What's the important takeaway from this? Should we now layer one more thing on top of what it means to be a software developer in the cloud these days? I've been thinking about this for a long time, but the idea of adding one more thing to worry about sounds pretty painful. Do we want all of our engineers agonizing over the cost of their code? Even in this new cloud world, the following quote from Donald Knuth is as true as ever.
We are far from seeing most organizations fully versed in and comfortable with AI as part of their company strategy. However, Asana and Anthropic have outlined five stages of AI maturity; a guide executives can use to gauge where their company stands in implementing real transformative outcomes. Many respondents say they’re in either the first or second stage. Only seven percent claim they’ve achieved the highest stage. ... Asana and Anthropic conclude that boosting comprehension is important, offering resources, training programs and support structures for knowledge workers to improve their education. Companies must also prioritize AI safety and reliability, meaning that AI vendors should be selected with “complete, integrated data models and invest in high-quality data pipelines and robust governance practices.” AI responses must be interpretable to facilitate decision-making and should always be controlled and directed by human operators. Other elements of organizations in Stage 5 include embracing a human-centered approach, developing strong comprehensive policies and principles to navigate AI adoption responsibly, and being able to measure AI’s impact and value
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A major problem with shadow AI is that users don’t read the privacy policy or terms of use before shoveling company data into unauthorized tools, she says. “Where that data goes, how it’s being stored, and what it may be used for in the future is still not very transparent,” she says. “What most everyday business users don’t necessarily understand is that these open AI technologies, the ones from a whole host of different companies that you can use in your browser, actually feed themselves off of the data that they’re ingesting.” ... Using AI, even officially licensed ones, means organizations need to have good data management practices in place, Simberkoff adds. An organization’s access controls need to limit employees from seeing sensitive information not necessary for them to do their jobs, she says, and longstanding security and privacy best practices still apply in the age of AI. Rolling out an AI, with its constant ingestion of data, is a stress test of a company’s security and privacy plans, she says. “This has become my mantra: AI is either the best friend or the worst enemy of a security or privacy officer,” she adds. “It really does drive home everything that has been a best practice for 20 years.”
As our software-powered world becomes more and more data-driven, unlocking and unblocking the coming decades of innovation hinges on data: how we collect it, exchange it, consolidate it, and use it. In a way, the speed, ease, and accuracy of data exchange has become the new Moore’s law. Safely and efficiently importing a myriad of data file types from thousands or even millions of different unmanaged external sources is a pervasive, growing problem. ... Data exchange and import solutions are designed to work seamlessly alongside traditional integration solutions. ETL tools integrate structured systems and databases and manage the ongoing transfer and synchronization of data records between these systems. Adding a solution for data-file exchange next to an ETL tool enables teams to facilitate the seamless import and exchange of variable unmanaged data files. The data exchange and ETL systems can be implemented on separate, independent, and parallel tracks, or so that the data-file exchange solution feeds the restructured, cleaned, and validated data into the ETL tool for further consolidation in downstream enterprise systems.
When we talk about AI, it’s essential to understand its fundamental workings—it operates based on the data it’s fed. Hence, the data input is crucial; it needs to be properly curated. Firstly, ensuring anonymisation is key; live customer data should never be directly integrated into the model to comply with regulatory standards. Secondly, regulatory compliance is paramount. We must ensure that the data we feed into the framework adheres to all relevant regulations. Lastly, many organisations grapple with outdated legacy tech stacks. It’s essential to modernise and streamline these systems to align with the requirements of contemporary AI technology. Also, mitigating bias in AI is crucial. Since the data we use is created by humans, biases can inadvertently seep into the algorithms. Addressing this issue requires careful consideration and proactive measures to ensure fairness and impartiality. ... It’s important for people to be highly aware of biases and misconceptions surrounding AI. We need to be conscious of the potential biases in AI systems.?