A key trend for our 2016 technology landscape will be machine learning and artificial intelligence (AI), if it isn’t a major driver in 2015 anyway that is. Right next to the term AI we will also find terms like deep learning, ‘neural’ networked computers and so-called cognitive computing.
As previously discussed on Forbes, cognitive computing describes the new breed of computers such as
Do computers still need us?
As computers get smarter and smarter, you might be asking yourself whether computers still need us to be there at all. Has automation developed to the point where humans have started to make themselves redundant?
Evans Data Corporation’s latest survey-driven communiqué suggests that the answer is no. Humans do still have a role to play on the planet. A total of 47% of the 529 developers surveyed reported that machine learning software requires human inputs some of the time -- and only 2.6% reported that human input was not required at all. Essentially then, we can say that 97.4 percent of computers will need the warmth of human touch.
To put this ‘finding’ into the context of the survey and original story theme, Evans is saying that software application developers involved in big data indicate that in projects that use machine learning techniques and processes, human input does not end at project deployment.
“Applications for artificial intelligence and machine learning may strike people as examples of completely autonomous computing, in which computers learn to process and act on data by themselves. In practice, development for machine learning is rather different from this fantasy. The overwhelming majority of developers involved in machine learning reveal that machine learning still requires a great deal of hands on involvement from programmers,” said Evans Data corporation director of research, Michael Rasalan. “A lot of the work in developing Machine Learning applications involves setting up big data systems and Hadoop implementations. But even after deployment, many developers will be creating and optimizing algorithms required to analyze massive amounts of data. This process is continuous and requires direct human input and control most of the time.”
Evans says its survey is a comprehensive study of software developers actively working with databases and analytics. It covers topics that revolve around the concept of big data including business cases for analytics, current data warehousing and ingestion solutions, approaches to analyzing and visualizing data etc.
Evan’s ‘Big Data and Analytics’ comes along in close proximity to news of Google open sourcing its TensorFlow technology. TensorFlow artificial intelligence software has been used internally back in the engine room both for Gmail and for the Google search function itself. TensorFlow comes now after we already know about the search giant’s development of its DistBelief software. DistBelief has been used to analyse, classify and subsequently automatically identify items contained within videos and photos, for example.
According to the Google developer blog, "TensorFlow is a highly scalable machine learning system. It can run on a single smartphone or across thousands of computers in datacentres. We use TensorFlow for everything from speech recognition in the Google app, to Smart Reply in Inbox, to search in Google Photos. It allows us to build and train neural nets up to five times faster than our first-generation system, so we can use it to improve our products much more quickly."
2016 predictions, brace yourself
We now collectively brace ourselves for the onslaught of 2016 technology predictions that come at this time of year. So of course AI and machine learning will feature as a key ‘disruptor’ alongside the race to 5G, centralized single cloud platforms plus robotics and wearables, probably.
Thankfully then, as fast as neural networks grow and cognitive computing is cool, but in a kind of scary way… the computers will still need us humans to be there. Well, 97.4 percent of the time they will.
