5 failed tech predictions for the 2010s that didn't work out

Futurist Gerd Leonhard predicted that tablets would usher in an era of augmented reality's dominance, which would be a "huge boon" to content production. AR has allowed for fun Snapchat effects and games like Pokemon Go, but it hasn't changed daily life in the way that people thought it would — at least not yet.

Autonomous vehicle technology from companies like Tesla has definitely improved, and reports of drivers falling asleep at the wheel have mostly been without injuries as the cars were able to compensate. But Tesla still says that autopilot mode requires "active driver supervision," and a Tesla in autopilot mode earlier this month crashed into a police car, proving that the system is far from perfect. Self-driving tech from Alphabet and Uber have also yet to see a wide launch, and largely remain in the testing phase.

The last decade has seen plenty of highs and lows for cryptocurrencies. Investing in bitcoin early could have made you very wealthy by now, but many analysts see it as a bubble or niche financial product.

Cryptocurrency exchanges have been hacked, sometimes leading to investors losing their holdings. Some early investors have made millions, others trying to get in on the craze have seen their investments fall to a fraction of their value as crypto prices fluctuate wildly.

Facebook is working on the launch of its Libra cryptocurrency, and CEO Mark Zuckerberg testified on the subject before the House Financial Services Committee in October. Lawmakers have been critical of the project, and many major backers including PayPal and Visa have dropped out.

Incorporating AI into sectors like policing was predicted to to help us avoid prejudice, but even as AI plays an increasingly important role in daily tasks, bias among AI exists. In fact, researchers keep finding evidence that AI is far from perfect and can introduce similar biases as those held by people. From an AI algorithm that kept black patients from getting the same quality of medical care as white patients, to hiring algorithms that learned to prefer male candidates, it's clearly early days for the technology.