The prevailing theory on the origin of our universe goes like this: about 13.7 billion years ago a single particle exploded. The resultant blast created an ever-expanding universe that, eventually, became home to the planet we call Earth.

The Big Bang theory first appeared in a scientific paper in 1931. Physicist Georges Lemaitre is credited with its creation. And the bulk of our assumptions about the universe and its rate of expansion are based on his ideas.

In 2019 that rate of expansion, called the Hubble Constant, was put into question by various teams that determined either the rate of expansion had been incorrectly calculated or something was seriously wrong with the universe.

Scientists are still sorting things out and working towards an explanation that can reconcile both the Big Bang and our modern observations.

The reason why we can’t just punch some numbers in a supercomputer and determine the truth is because we don’t have all the information.

Trying to determine how old the universe is by measuring its current rate of expansion is like trying to pick the winner of a NASCAR race based on a blurry, out-of-context photograph of one racer’s left rear tire.

To that end, the Big Bang theory only really works if we assume it was the beginning of our universe. Doing so makes it the one piece of the entire puzzle that corresponds with what we’re actually able to see and measure.

But what if the Big Bang wasn’t the beginning?

Chanda Prescod-Weinstein, a physicist at the University of New Hampshire, has a differing theory.

Writing for New Scientist , they claim it makes more sense to assume the universe has been expanding forever.

Per the article:

The ramifications of such a theory may seem trivial – one explanation for a number is as good as another until we’re able to measure more. But a lot of our assumptions concerning both classical and quantum physics are grounded in the idea that time is more than just a construct.

Whether we’re discussing Newton’s Laws or breaking down the nature of relative observations in quantum physics , the idea is that there’s a dimensional quality called time that’s codified by distinct points representing the beginning and end of an event.

Without a finite moment at the creation of the universe where nothingness became something, there’s no origin point for time – there are no beginnings.

The concept of infinite expansion without a beginning may be difficult to wrap our heads around, but it kind of adds up. After all, it seems paradoxical to imagine a period in which the universe itself, and thus time, didn’t exist at all because you’re forced to wonder how long time didn’t exist for before it finally did .

But, if time’s always existed – because the universe itself has always existed – then perhaps it’s never existed. What is time without a beginning or end?

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Dear CEOs, you’re getting ripped off by legal AI scams

What if I told you I was selling a set of computer programs that could automagically solve all of your hiring, diversity, and management problems overnight? You’d be stupid not to at least listen to the rest of the offer, right?

Of course no such system exists. The vast majority of AI products purported to predict social outcomes are blatant scams. The fact that most of them are legal doesn’t stop them from being snake oil.

Typically, the following AI systems fall under the “legal snake oil” category:

AI that predicts recidivism

AI that predicts job success

Predictive policing

AI that predicts whether an individual will become a criminal or terrorist

AI that predicts outcomes for children

The reason for this is simple: AI cannot do anything a human (given enough time and resources) could not themselves do. Artificial intelligence is not psychic and it cannot predict social outcomes.

As associate professor of computer science at Princeton University Arvind Narayanan said in a series of recent lectures on snake oil AI:

Think about it, have you ever heard of a big business that’s never made a single hiring mistake?

These systems work on the same principle as the magic beans from Jack and the Beanstalk . You have to install the systems, pay for them, and then use them for an extended period of time before you can evaluate their effectiveness.

That means you’re being sold on statistics up front. And, when it comes to benchmarking black box AI systems , you may as well be measuring how much mana it takes to cast a fireball spell or counting how many angels can dance on the head of a pin: there’s no science to be done.

Take HireVue, one of the most popular AI-hiring system vendors in the world. Its platform can purportedly measure everything from “leadership potential” to “personality” and “work style” from a combination of video interviews and games.

That sounds pretty fancy, and HireVue’s statistical claims all seem quite impressive. But the bottom line is that AI can’t do any of those things.

The AI doesn’t measure candidate quality, it measures a candidate’s adherence to an arbitrary set of rules decided on by the platform’s developers.

Here’s a snippet from a recent article by the Financial Times’ Sarah O’Connor that explains how silly the video interview process really is:

Unless you’re being hired to be a TV news anchorperson, this is ridiculous.

“ Energy” and “personality” are subjective ideas that can’t possibly be measured, as is “authenticity” when it comes to humans.

HireVue’s systems, like all AI purported to predict social outcomes, are nothing more than arbitrary discriminators.

If the only “good” candidates are those who smile, maintain eye contact, and exhibit the right “authenticity” and “energy,” then candidates with muscular, neurological, or nervous system disorders who can’t do those things are instantly excluded. Candidates who don’t present as neurotypical on camera are excluded. And candidates who are culturally diverse from the creators of the software are excluded.

So why do CEOs and HR leaders still insist on using AI-powered hiring solutions? There are two simple reasons:

Here are some other scientific and (well-sourced) journalistic resources explaining why AI purported to predict social outcomes is almost always a scam:

Artificial Intelligence in the job interview process (University of Sussex)

Machine Bias (ProPublica)

How to recognize AI snake oil (Princeton University)

The Making of AI Snake Oil (Toward Data Science)

Data and Algorithms at Work: The Case for Worker Technology Rights (UC Berkeley)

An AI to stop hiring bias could be bad news for disabled people (Wired)

AI can’t predict how a child’s life will turn out even with a ton of data (MIT Technology Review)

Measuring the predictability of life outcomes with a scientific mass collaboration (Princeton University)

AI can’t tell if you’re lying – anyone who says otherwise is selling something (Neural)

Hiring Algorithms Are Not Neutral (Harvard Business Review)

The Perils of Predictive Policing (Georgetown Public Policy Review)

Predictive policing is a scam that perpetuates systemic bias (Neural)

IBM’s new sustainability accelerator is a blueprint for corporate responsibility

Nearly all of the world’s top tech companies are invested in solving the biggest problems facing the planet. But not all solutions are created equal. The issues concerning Silicon Valley, London, or Beijing are vastly different than those faced by smaller, less profitable communities around the globe.

That’s why, in the modern era, the idea of corporate responsibility has to go beyond charitable donations and one-off events to raise awareness. The most vulnerable communities need more than just cash. They need sustainable solutions to long-term problems.

Enter Big Blue

IBM’s new “Sustainability Accelerator” is a program designed to give under-serviced communities an advantage in the fight against the human-wrought climate and energy crisis, natural disasters, and the devastating effects of pollution, with an eye towards the future.

In order to accomplish this, IBM is soliciting requests for proposals (RFPs) to create sustainability programs from local governments and nonprofits around the globe.

Organizations which are selected will receive help at every level of their proposal from inception to implementation, including pro bono support from IBM services, technicians, and trainers.

IBM’s Justina Nixon-Saintil, Vice President and Global Head of Corporate Social Responsibility, told Neural:

That’s a huge chunk of change, but the bigger value may come in the form of IBM technology.

The partners chosen will work directly with IBM to develop their projects with services ranging from Watson AI products to cloud-based computing and everything inbetween.

Nixon-Saintil told Neural the accelerator would even consider implementing IBM quantum technologies if the solution called for it.

The current RFP round, which ends on 30 April, is focused on “clean energy solutions benefiting vulnerable populations.”

Who gets in?

According to Nixon-Saintil, the point of the program is to take advantage of the technologies IBM offers to its biggest clients by making them accessible to vulnerable communities, and then to scale those efforts to other areas facing similar problems.

Accordingly, the proposals with the strongest chance of being accepted will likely be those that bring aid to areas that need it most.

Luckily though, organizations considering applying for the accelerator don’t have to make it up as they go along.

IBM worked with three non-profit organizations during a trial program leading up to the sustainability accelerator’s launch in order to dial things in.

The trial round focused on solutions for agricultural sustainability problems that addressed climate change, pollution, and crop yields.

What do they get?

The proposals selected for the accelerator will get the works. IBM engineers and technicians will help them develop their rough ideas into actionable solutions and Big Blue will foot the bill for just about everything.

According to an IBM blog post :

Neural’s take : when it comes to tech for good, it doesn’t get much better than this. Programs that throw tax-deductible donations at problems and then leave the recipients to fend for themselves might be well-meaning, but creating change at scale takes more than that.

IBM intends to provide the know-how, equipment, services, and skill-training necessary for the communities participating to eventually support their own solutions. And, according to IBM, the intent is for the most effective methods to be fine-tuned, scaled, and implemented elsewhere.

Basically, this accelerator is also an incubator. Instead of IBM trying to figure out how to solve everyone else’s problems, it’s empowering communities to come up with their own solutions — it’s IBM-as-a-service as much as it’s IBM at your service. We love it.

You can get more information here on IBM’s website .