Artificial intelligence is a sector that will continue to grow due to its value in the world. Max Tegmark, the President of the Future of Life Institute, states “Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as we manage to keep the technology beneficial.“
Indeed, artificial intelligence is infiltrating every area of our lives. We see it in Google Assistant, in Alexa, in SIRI, and Cortana. Several automotive and transportation companies such as Tesla, Rivian, Cruise Automation, Uber, Waymo, and a few others focus exclusively on automation or artificial intelligence as a near-future inevitability. Softbank, with its several vision funds, has one goal, accelerate and scale artificial intelligence to create a harmonious society.
As such, we are likely to see more artificial intelligence integrations within our world.
Artificial intelligence at the current moment is focused on specific tasks like beating Garry Kasparov at the ancient game of chess or other humans at the game of go. The idea is to improve its intelligence and bring about a more broad and comprehensive artificial intelligence machine. General artificial intelligence is what many fear. General artificial intelligence is where we can end up with variations of the Terminator, with machines that have the same cognitive capabilities of humans.
But to get to this level, more companies will have to step in, invest and push artificial intelligence forward.
Entities such as Scale.AI, seek to do just that with their operations.
Scale.AI and Computing
Scale.AI believes that machine learning will usher in a new era of computing. The organization thinks that computational machines, guided by humans, can identify images, audio, conduct translation tasks, and tackle more narrow task activities. At the same time, Scale.AI will state that while machine learning has tremendous capabilities from now into the future, they must kick-start and drive the machine learning value.
As such, Scale.AI focuses on a scalable but straightforward task at the outset. The top management of the company will focus on “labeled data” initiatives. They will focus on this task because this piece of the artificial intelligence puzzle is, according to the company, “even more essential than algorithms.”
But is data labeling so important?
Identification, Classification, Categorization and Artificial Intelligence
Artificial intelligence machines are only as capable as the data sets placed into the system. Proper data sets plus the right training leads to the correct results. The right training involves the specific use of identification, classification, and categorization of data.
These (labeled datasets) allow for the utilization of meta-programming, where the developer programs the computer to program. The better the labeled datasets, the better the results. This demand for data and labeled datasets drives the existence of Scale.AI.
The company focuses on this foundation of datasets to scale artificial intelligence.