Neural nets trained on sensitive data, like medical data or social security numbers, can “accidentally” memorize it, leaving it vulnerable to hackers.
“We hope to raise awareness that it’s important to consider protecting users’ sensitive data as machine learning models are trained. Machine learning or deep learning models could be remembering sensitive data if no special care is taken.”
There is a lot of discussions the past years about the revolution predictive analytics will bring to the businesses. But, predictive analytics is not something new. It used to be a part of statistics! Corporations with the resources were doing it for decades. Nowadays, however, it is more accessible, due to technological advancements in the areas of computing power, software and storage, the abundance of big datasets, and advances in algorithm research. Any student can now use the cloud to perform an experiment that a few years ago would have been possible to be performed only by large corporations or government agencies.
The challenge ishow can we apply advanced analytics, and AI in an enterprise environment, efficiently and effectively.
The first time I saw predictive analytics live, in praxis, to provide useful information was in the mid-90s. As a student and aspiring engineer, I was enrolled in a Total Quality/Six Sigma class. During a field trip we visited a Japanese run, car manufacturing plant where, to my astonishment, blue-collar workers were applying regression in order to predict when to change tools!
“Come and join us, and we will show you how to predict the future”.
I tend to get invitations like this all time. Friendly people from marketing want to meet me and eventually sell tools or services to predict the future. So, it makes me wonder, if they actually have a machine to predict the future, why on earth are they spending their energy selling software and organizing events? Perhaps, you could pause for a second and consider what would you do if you had such a machine?
Although artificial intelligence has led to remarkable achievements in recent years, expectations for what the field is able and will be able to achieve in the next decade tend to run much higher than what will actually turn out to be possible. While some world-changing applications like autonomous cars are already within reach, many more are likely to remain elusive for a long time.
A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data.
Time Series Data obviously has to do with time and the first thing that comes to mind is finance. The world of finance is the world of time series, so stocks, and currency exchange rates and interest rates, they’re all Time series.
A lot of people are using the terms Artificial Intelligence and Machine Learning interchangeably or to mean different things. This adds confusion to the complexity of modern technology.
When Google DeepMind’s AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go, the terms AI, machine learning, and deep learning were used in the media to describe how DeepMind won. And all three are part of the reason why AlphaGo trounced Lee Se-Dol. But they are not the same things.