Breaking up is hard to do

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The time immediately after a bad relationship is filled with promise. It's as if you've rid yourself of something that was weighing you down and keeping you from reaching your full potential. You fell light and clear and free. But this honeymoon with yourself is short-lived and you’re soon in a new relationship fraught with the same old problems. This pattern continues until you finally realize that most of the issues are your own, and that to be truly free, you must break up with yourself.

Andrew Boyd, Daily Afflictions

17 Recent Articles about Using AI

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Stop Deploying General-Purpose AI Models For Everything

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Ensuring safe A.I. is another reason developers should stop deploying general-purpose models for everything. To date, the industry has been unable to guarantee that generative A.I. systems will stick to their safety instructions. Studies have documented instances in which generative A.I. deceives its human operators, tries to use blackmail if its self-preservation is threatened and responds in a way that could lead to murder. More specialized systems like AlphaFold and Waymo’s driving systems won’t misbehave that way because their operating parameters are much narrower. - Gary Marcus writing in the New York Times

When we are most likely to do something regrettable

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Under the right circumstances, a subconscious neurobiological sequence in our brains causes us to perceive the world around us in ways that contradict objective reality, distorting what we see and hear. This powerful shift in perception is unrelated to our intelligence, morals, or past behaviors. In fact, we don’t even know it’s happening, nor can we control it. 

Researchers found that it happens in two distinct situations: those involving high anxiety and those associated with major reward. Under these conditions, all of us would do something just as regrettable as those headline-grabbing stories, contrary to what we tell ourselves. Phrased differently, we don’t consciously decide to act a fool. Rather, once our perception is distorted, we act in ways that seem reasonable to us but foolish to observers.

Robert Pearl writing in Vox

10 New Jobs that may Emerge from AI

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AI assessors – Someone in this role will evaluate models, keeping track of how they’ve improved, what they are best at doing, and how much they are hallucinating.

AI auditors – Someone who dig down into the A.I. to understand what it is doing and why and can then document it for technical, explanatory or liability purposes.  

AI consistency coordinator – This job is about ensuring digital replicas remains  consistent as changes are made.

AI consultants – This job involves helping businesses adopt and implement AI by offering a strategic roadmap, technical expertise, and project leadership. The AI consultant must facilitate communication between a company’s departments to marry technical knowledge with business needs.  After deployment of AI, it is their job to help set up ways to monitor the outcomes. Besides possessing a robust AI education, the AI consultant will have to stay on top of trends and changes in the industry.

AI engineers – Unlike traditional IT roles, people in this position will fix the AI when it breaks, digging through the layers to determine what went awry, why it went wrong and how to repair it. Like a plumber, they’ll snake the pipes to clear out the system and figure out how to avoid the problem next time. This will be particularly important when it comes to models that have been highly customized to the organization.

AI ethicist – This role will involve building chains of defensible logic that can be used to support decisions made by AI (or by hybrid A.I.-and-human teams) to address both ethical and legal concerns.

AI integrators – These are experts who figure out how to best use AI in a company, then implement it. These jobs will be technical in nature, requiring a deep understanding AI while possession a knowledge of the company so that that AI can meet real business needs.

AI personality director – This person fine-tune the “personality” of the AI so that its style of interacting with employees and customers fits with the organization’s ethos. This can become an integral part of a company’s branding.

AI trainer – This is the job of helping the AI find and digest the best, most useful data and then teach the AI to respond in accurate and helpful ways.

AI translator (trust director) – People who understand AI well enough to explain its mechanics to others in the business, particularly to leaders and managers, so that they can make effective decisions. These workers will not only explain what the AI output means (especially when it is technical) but how trustworthy the information and conclusions are. This role may fall under that of compliance officer, helping organizations understand contracts and report written by AI.  

Read more at The New York Times

18 Surprising Things AI can do now

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How AI is Changing Entry Level Jobs

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Rather than have rookie employees compile reports or write memos — things the A.I. is good at — you might have them start, say, creating new ideas for products right away. Traditionally, this kind of work would be reserved for deeply experienced workers, but it won’t need to stay that way. By empowering young, inexperienced workers, A.I. can enable them to be more entrepreneurial, faster. And this means that a greater range of the organization — with a wider range of perspectives — can be hunting for new great ideas or new areas for growth rather than busying themselves with repetitive office tasks. -New York Times 

AI Definitions: Big Data

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Big Data - Data that’s too big to fit on a single server. Typically, it is unstructured and fast-moving. In contrast, small data fits on a single server, is already in structured form (rows and columns), and changes relatively infrequently. If you are working in Excel, you are doing small data. Two NASA researchers (Michael Cox and David Ellsworth) first wrote in a 1997 paper that when there’s too much information to fit into memory or local hard disks, “We call this the problem of big data.” Many companies wind up with big data, not because they need it, they just haven’t bothered to delete it. Thus, big data is sometimes defined as “when the cost of keeping data around is less than the cost of figuring out what to throw away.”    

Big Data looks to collect and manage large amounts of varied data to serve large-scale web applications and vast sensor networks. Meanwhile, data science looks to create models that capture the underlying patterns of complex systems and codify those models into working applications. Although big data and data science both offer the potential to produce value from data, the fundamental difference between them can be summarized in one statement: collecting does not mean discovering. Big data collects. Data science discovers.  

More AI definitions here

Form a strong identity … and let it go

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Fixating on one part of your identity and saying, “I am this—and this is all I am” stagnates your identity. A more holistic approach allows your identity to shift and change and expand as you become more fully who you are. Different experiences and people will draw different things out of you. Yes, form a strong identity and find words that help you express where you are at in this moment. But do it in order to ultimately let it go.

The Trivial & the Bureaucratic

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The Bikeshed Effect (focusing on the trivial to the neglect of the important) is a spiral toward the insignificant.

The time and energy waster grows from a lack of working from priorities. If you don’t continuously cut off its oxygen, you adopt to the surrounding culture that fuels spotlighting the details.   

The Bikeshed Effect is related to Parkinson’s Law, which suggests a project will take as long as is given to finish it. The further out the deadline, the longer it will take to complete a task. Thus, bureaucracy expands to use up whatever resources are devoted to it.

To get at what’s underneath Parkinson’s Law and the Bikeshed Effect, why we focus on the trivial and put off deadlines, we must ask ourselves, “What are we afraid of?” Sabina Nawaz wrote in the Harvard Business Review:

When we’re scared, we might spin up a frantic list of activities to avoid confronting our fear. The more afraid we are, the more we retreat from what spooks us by believing we’re too busy to tackle it.

To escape the ranks of the fearful and dead bureaucrats, take a serious look at the angst underneath and disempower it.

Making us Average

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A.I. is a technology of averages: large language models are trained to spot patterns across vast tracts of data; the answers they produce tend toward consensus, both in the quality of the writing, which is often riddled with clichés and banalities, and in the caliber of the ideas. Other, older technologies have aided and perhaps enfeebled writers, of course—one could say the same about, say, SparkNotes or a computer keyboard. But with A.I. we’re so thoroughly able to outsource our thinking that it makes us more average, too. - Kyle Chayka writing in the New Yorker

AI Definitions: AI model collapse

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AI model collapse - The idea that AI can eat itself by running out of fresh data, so that it begins to train on it’s on product or the product of another AI. This would magnify errors and bias and make rare data more likely to be lost, leading to an erosion of diversity—not only ethnic diversity but linguistic diversity as the AI model’s vocabulary shrinks and its grammatical structure becomes less varied. In effect, the model becomes poisoned with its own projection of reality. AI-generated data is often a poor substitute for the real thing. Example

More AI definitions here

18 Articles about AI & the Creative Arts

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Amid the A.I. Deluge, What Counts as Art? Ask the Curators. - New York Times

Indonesia’s film industry embraces AI to make Hollywood-style movies for cheap – Rest of World  

Let's talk about AI art. – The Oatmeal

I’m a Screenwriter. Is It All Right if I Use A.I.? – New York Times

DC Comics won’t support generative AI: ‘not now, not ever’ – The Verge  

Inside the work of an AI content creator as online video gets unreal – Washington Post 

Publishers with AI licensing deals have seven times the clickthrough rate – Press Gazette

Is this the end of Adobe as we know it? Unless Adobe listens to users it could be – Amateur Photographer  

When A.I. Came for Hollywood - New York Times

I didn’t believe the hype about Google Mixboard — now I’m obsessed - Tom’s Guide

In an era of AI slop and mid TV, is it time for cultural snobbery to make a comeback? – The Guardian

Creator of AI Actress Tilly Norwood Responds to Backlash: “She Is Not a Replacement for a Human Being” – Hollywood Reporter

A short video from the UK’s Particle6 featuring AI ‘Actor’ Tilly Norwood (and is completely AI generated) - Particle6 TV

The Psychology Of Trust In AI: A Guide To Measuring And Designing For User Confidence – Smashing Magazine

Record labels claim AI generator Suno illegally ripped their songs from YouTube – The Verge

Artists are losing work, wages, and hope as bosses and clients embrace AI – Blood in the Machine

How AI is disrupting the photography business – Axios

Writing alt text with AI - Jared Cunha