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
Tag Archives: Augmented Intelligence
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
The Secret to AI Is People
Too many business leaders still believe that AI is just another ‘plug and play’ incremental technological investment. In reality, gaining a competitive advantage through AI requires organizational transformation of the kind exemplified by companies leading in this era: Google, Haier, Apple, Zappos, and Siemens. These companies don’t just have better technology — they have transformed the way they do business so that human resources can be augmented with machine powers.
How do they do it? To find out, we conducted a multistage study over five years, beginning with a survey of senior managers and executives, followed by interviews and surveys across a wide range of industries to identify technology implementation strategies and barriers, and in-depth studies of five leading organizations. Our key takeaway is counterintuitive. Competing in the age of AI is not about being technology-driven per se — it’s a question of new organizational structures that use technology to bring out the best in people. The secret to making this work, we learned, is the business model itself, where machines and humans are integrated to complement each other. Machines do repetitive and automated tasks and will always be more precise and faster. Read More
Can artificial intelligence prompt a creative revolution?
It’s an unlikely partnership, but AI can stimulate a ‘new brand’ of human creativity.
- Thanks to our ability to contextualize, think metaphorically and define new patterns, human creativity remains distinguished from machine’s
- Creative transformation using AI is not a one-off change, but a long-form process
- Through freeing up our time, giving us prompts and future-proofing our efforts, AI may help us realize new, exciting, creative directions
Consistent Estimators for Learning to Defer to an Expert
Learning algorithms are often used in conjunction with expert decision makers in practical scenarios, however this fact is largely ignored when designing these algorithms. In this paper we explore how to learn predictors that can either predict or choose to defer the decision to a downstream expert. Given only samples of the expert’s decisions, we give a procedure based on learning a classifier and a rejector and analyze it theoretically. Our approach is based on a novel reduction to cost sensitive learning where we give a consistent surrogate loss for cost sensitive learning that generalizes the cross entropy loss. We show the effectiveness of our approach on a variety of experimental tasks. Read More
Augmented Intelligence is the New Intelligence
What does it mean when shifting from AI to augmented intelligence?
The future of decision-making includes an inventive blend of information, analytics, and artificial intelligence (AI), with the perfect scramble of human judgment. The outcome is augmented intelligence, where the analytical force and speed of AI assumes control over most of data processing, controlling human workers to make progressively agile, more intelligent choices and find new discoveries. Read More
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