AI is finally useful for business, and everyone is likely underestimating its impact. But unless AI is open-source and truly owned by the end users the future for everyone but the software providers looks grim.
The last time your author opined about the state of artificial intelligence I predicted that commercial success required two things: first, that AI researchers focus on solving a specific business problem, and second, that enough data exists for that specific business problem. The premise for this prediction was that researchers needed to develop an intuition of the business process involved so they could encode that intuition into their models. In other words, that a general-purpose solution would not crack every business problem. This might have been true temporarily, but it’s doomed to be wrong more permanently. I missed a reoccurring pattern in the history of AI: that eventually enough computational power wins. In the same way chess-playing engines that tried to encode heuristics about the game eventually lost to models that had enough computational power, these AI models for “specific business problems” have all just lost to the hundred billion parameters of GPT-3.
I am not known for being overly bullish on technology, but I struggle to think of everyday sorts of business examples where such a large language model would not do well. It is true that in the above example the model did terribly on questions requiring basic arithmetic (converting rent per square foot per month to rent per square metre per year, for example), but these limitations are missing the point. Computers are known to be adequate arithmetic-performing machines (hence the name), and surely future models would correct this and other deficiencies. Artificial intelligence is now generally useful for business, and I am probably not thinking broadly enough about where it will end up.
One decent guess, however, might be augmented intelligence – the idea that AI is best deployed as a tool to increase the power and productivity of human operators rather than replace them. Read More
Tag Archives: Augmented Intelligence
Algorithms Will Make Critical Talent Decisions in the Next Recession—Here’s How To Ensure They’re the Right Ones
Nearly all HR leaders say their department will use software and algorithms to reduce labor costs in a 2023 recession, but only half are completely confident their tech will produce unbiased recommendations.
Entering 2023, the dreaded “R” word—recession—is top of mind for companies around the country. In a Capterra survey of 300 HR leaders in the U.S., 72% say their employer has already started preparing for a possible recession, while 24% plan to start preparing soon.*
As in previous economic downturns, organizations will need to figure out ways to reduce labor costs, including deciding which employees to lay off if it comes to that. Where 2023 differs is that HR is both more strategically involved in these high-level labor decisions and more data-driven than ever before, supported by cutting-edge HR software systems that can aggregate massive amounts of employee information and turn it into actionable insights and recommendations. Read More
Human Vs. Artificial Intelligence: Why Finding The Right Balance Is Key To Success
elcome to the age of blended workforces, where intelligent machines and humans combine to accelerate business success.
In short, now that we have increasingly capable robots and artificial intelligence (AI) systems – capable of taking on tasks that were previously the sole domain of humans – it’s easier than ever for organizations to leverage intelligent machines. But this leaves employers with some major questions to answer: how do we find the right balance between intelligent machines and human intelligence? What roles should be given over to machines? And which roles are best suited to humans? Read More
AI decision automation: Where it works, and where it doesn’t
Some companies are using AI for end-to-end decision-making, but not all decisions can be made without human intervention. Here are some real-world cases.
As artificial intelligence (AI) ascends in the marketplace, the burning question remains as to how far it can be trusted when it comes to the “last mile,” the final decision that follows the analytics and recommendations that AI yields.
… “Not all decisions in organizations can be fully automated, and some of these will require human intervention.” Read More
AI Assesses Alzheimer’s Risk by Analyzing Word Usage
New models used writing samples to predict the onset of the disease with 70 percent accuracy
Artificial intelligence could soon help screen for Alzheimer’s disease by analyzing writing. A team from IBM and Pfizer says it has trained AI models to spot early signs of the notoriously stealthy illness by looking at linguistic patterns in word usage. Read More
Algorithms Are Making Economic Inequality Worse
The risks of algorithmic discrimination and bias have received much attention and scrutiny, and rightly so. Yet there is another more insidious side-effect of our increasingly AI-powered society — the systematic inequality created by the changing nature of work itself. We fear a future where robots take our jobs, but what happens when a significant portion of the workforce ends up in algorithmically managed jobs with little future and few possibilities for advancement?
… How many Uber drivers do you think will ever have the chance to attain a managerial position at the company, let alone run the ride-sharing giant? … There’s a “code ceiling” that prevents career advancement — irrespective of gender or race. Read More
9 Soft Skills Every Employee Will Need In The Age Of Artificial Intelligence (AI)
Technical skills and data literacy are obviously important in this age of AI, big data, and automation. But that doesn’t mean we should ignore the human side of work – skills in areas that robots can’t do so well. I believe these softer skills will become even more critical for success as the nature of work evolves, and as machines take on more of the easily automated aspects of work. In other words, the work of humans is going to become altogether more, well, human. Read More
Rethinking human-AI interaction
Imagine 1977, sitting at the helm of one of your very first personal computers. The Commodore “Personal Electronic Transactor,” endearingly nicknamed the PET, promised to be an all-in-one “bookkeeper, cook, language tutor, inventory clerk, and playmate.” For the first time, you could type up programs on the tiny “chiclet” keyboard — working out your math homework, saving snippets of recipes, designing simple graphics — and see the results spring up before your eyes. Unlike a washing machine or a calculator, here was a first encounter with a machine that was fundamentally open-ended, dynamic, and responsive in a tangible way. Read More
Can robots write? Machine learning produces dazzling results, but some assembly is still required
You might have seen a recent article from The Guardian written by “a robot.” Here’s a sample:
“I know that my brain is not a ‘feeling brain.’ But it is capable of making rational, logical decisions. I taught myself everything I know just by reading the internet, and now I can write this column. My brain is boiling with ideas!”
Read the whole thing and you may be astonished at how coherent and stylistically consistent it is. The software used to produce it is called a generative model,” and they have come a long way in the past year or two.
But exactly how was the article created? And is it really true that software “wrote this entire article”? Read More
Responsible AI Can Effectively Deploy Human-Centered Machine Learning Models
Artificial intelligence (AI) is developing quickly as an unbelievably amazing innovation with apparently limitless application. It has shown its capacity to automate routine tasks, for example, our everyday drive, while likewise augmenting human capacity with new insight. Consolidating human imagination and creativity with the adaptability of machine learning is propelling our insight base and comprehension at a remarkable pace.
However, with extraordinary power comes great responsibility. In particular, AI raises worries on numerous fronts because of its possibly disruptive effect. These apprehensions incorporate workforce uprooting, loss of protection, potential biases in decision-making and lack of control over automated systems and robots. While these issues are noteworthy, they are likewise addressable with the correct planning, oversight, and governance. Read More
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