Of all the jets produced by the United States, few are as well known and beloved as the SR-71 Blackbird, first developed by Lockheed Corporation’s Skunk Works in the 1960s. The SR-71 is best known for being incredibly fast, having set numerous as yet unbroken speed records under various conditions throughout its time in operation, which ended with NASA in 1999. The SR-71’s speed is difficult to imagine, as it was capable of reaching Mach 3.3 (2,193.2 mph).
That’s around four times the average speed of a passenger airline, so it’s pretty fast. In fact, the SR-71 was so fast that one of the aircraft managed to break the radar screen of a jet trying to track it in the 1960s. This came during the Vietnam War, when an SR-71 exited Chinese airspace at a time when the Navy required at least two fighters to be airborne over the Gulf of Tonkin. The mission type was designated as BARCAP, or Barrier Combat Air Patrol, and when the SR-71 left Chinese airspace, one pilot operating their F-4B Phantom II fighter jet attempted to track it.
This wasn’t for nefarious reasons, but knowing that the top-secret SR-71 was about to fly into their airspace was tantalizing enough to extend the antenna as far as it would go to see the aircraft zip by. Unfortunately, the SR-71 moved so quickly that it actually broke the fighter jet’s radar screen — it simply couldn’t handle monitoring an object moving that quickly through the air.
How an F-4B Phantom II’s radar screen failed upon seeing an SR-71
Jerry Hart, a former F-4B Phantom pilot explained what happened in an interview with The Aviation Geek Club. He described flying (Barrier Combat Air Patrol (BARCAP) missions as being typically dull, so it makes sense that an SR-71 sighting would entice him to try and track it. After turning north to face where the SR-71 would be, Hart explained how his attempt to track it went down:
“I watched in amazement as the Vc circle, which told us the relative closing velocity of the target, started rapidly winding up far higher than I had ever seen it before. Then the B trace, which indicated the target’s position relative to the nose of our aircraft, began to dither back and forth rapidly before slamming to the side of the scope, and the screen went completely black. We never could get it to work again.” Once instrumentation completely failed, Hart opted to use his Mark I eyeballs to see the elusive Blackbird.
Unfortunately, the SR-71 flew too high to be seen with the naked eye. This story comes from a former pilot who once positively identified an aircraft at 56 nautical miles (64.4 miles), so his inability to see the SR-71 suggests it was way too high and moving far too fast to see. That’s what the SR-71 was designed for — it had to fly high and fast to elude Soviet anti-aircraft systems. It’s not surprising to learn that it was difficult to find and see, even with prior notice that it was coming.
Staff who use AI can end up with more to do, not less.
Think carefully about the tools you’re using and why.
Adopt a set of standards and refine your outputs.
The promise of productivity boosts from AI can come with an unwelcome side order of stress. Harvard Business Review found that AI doesn’t reduce work; it intensifies it, leading to cognitive fatigue and unsustainable hours.
While the common perception is that AI can help reduce workloads, allowing employees to focus more on higher-value and more engaging tasks, HBR’s research found that staff using AI worked more quickly and often ended up with more to do, not less.
Ankur Anand, group CIO at tech recruiter Harvey Nash, said professionals who want to avoid cognitive fatigue must understand how to use AI effectively and its potential risks.
“That focus will help to reduce the noise around the workload that AI creates,” he told ZDNET, suggesting that many people have unrealistic expectations about the productivity boost that AI will provide.
“Many organizations are telling their people, ‘We want to understand how you’re making an impact with AI,'” he said. “But these professionals are not empowered, which means that using AI adds a lot of pressure, because they need to prove themselves on their own terms.”
If you’re going to make the most of AI at work, then you’re going to have to find an effective balance between completing tasks quickly and producing high-quality work.
Here’s how the experts believe professionals can ensure they reap the benefits, not the problems, of AI — and they suggest that you’ll need to focus on three core areas: tools, guidelines, and outputs.
Limit your toolset
Alex Read, senior enterprise product manager for data at energy provider EDF UK, told ZDNET that the best way for professionals to reap the benefits, not the challenges, of AI is to be uber-focused on tools that help you produce value in your roles.
While there are thousands of potential AI-enabled services on the market, Read said sensible professionals limit their horizons.
In his own role, for example, Read focuses on how AI can help him build a data platform and update information accurately, efficiently, and productively: “Anything outside of that scope is noise for me.”
That sentiment resonated with Nick Pearson, CIO at technology specialist Ricoh Europe, who told ZDNET it’s important to take a step back and think carefully about how an AI tool can help you produce value in your role.
“If you think about the phrase ‘gen AI,’ the tech is very good, by definition, at generating outputs,” he said. “I could go to bed in the evening, set the model to work, and we could have four new IT strategies produced overnight.”
However, quantity doesn’t necessarily mean quality. Pearson suggested it’s important to focus on AI’s blind spots, particularly as most models are trained on preexisting content.
“AI can’t inspire people, per se; it can’t naturally create something new, because it’s actually quite recursive,” he said.
“And the judgment you have to put in sometimes, on top of everything else, whether it be an ethical or a capability judgment, is not there automatically in the technology.”
It’s in this gap, said Pearson, that human experts play a critical role: “We’re toying with that concern as an organization and saying, ‘Where does AI really play an important role, versus where are we upskilling people in areas that AI probably won’t play for a long time?'”
To correct this issue, HBR said companies need to adopt an “AI practice,” or a set of norms and standards around AI use that help professionals ensure they use AI in a constrained but productive manner.
At EDF UK, Read is part of an internal AI Center of Excellence in enterprise IT, which enables policy for the effective use of AI across the wider organization.
In addition to Read, who contributes input from a data-use perspective, the group includes other tech representatives, such as the firm’s senior manager of AI, principal software engineer, and principal solution architect.
“The remit of this center is to make sure that, when the federated business units are looking to build, develop, and deploy AI services, they have platforms, guidance, best practices, architectural assets, and materials to guide them on how to safely and efficiently adopt AI and operationalize it at scale,” he said.
Some of the key themes the center considers when assessing AI tools are scalability and reusability, ensuring a proposed service doesn’t replicate one already in use.
“All new tools and services related to AI will go through that hopper and funnel to understand scope and ensure the security, regulatory, and ethical side of things are understood,” he said, suggesting that all professionals should use their organization’s pre-existing guidelines to foster an appropriate exploitation of emerging tech.
“The benefit that guided approach brings is that it allows us to be clear in our messaging around what AI services can be used, how they’re used from a use-case perspective, and ultimately, what personas are allowed to use them.”
Louise Newbury-Smith, head of UK&I at technology specialist Zoom, told ZDNET that one way to ensure your outputs are constrained is to focus on prompting.
“Use simple amendments to be specific, such as ‘Give me the top three things with the biggest impact.’ That approach should guide your prompt, rather than saying, ‘Give me everything you know about this topic.'”
Newbury-Smith said the successful use of AI is all about being smart about how it’s exploited, and that effectiveness comes down to enablement and engagement. If a prompt yields too much information, refine it until you get what you need. She said this should still be faster than trying to get answers without AI.
The basic message for professionals is that effective applications of AI are all about you staying in the loop, said Bernhard Seiser, vice president of digital, data, and IT at AOP Health.
Think before you use AI, and think again before you push your outputs around the organization.
“It doesn’t help the business if you get AI-generated emails that are many pages long, and then you need ChatGPT to summarize the text,” he told ZDNET.
Seiser said that while there are certain tasks generative AI is good at and worth using for, in the end, “you need to use your brain.”
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