- Using Deeplearning4J, you can create convolutional neural networks, also referred to as CNNs or ConvNets, in just a few lines of code.
- If you don’t know what a CNN is, for now, just think of it as a feed-forward neural network that is optimized for tasks such as image classification and natural language processing.
- If you want to list all the labels present in the dataset, you can use the following code:
At this point, if you compile and run your project, you should see the following output:
It’s now time to start creating the individual layers of our neural network.
- Another important thing to note in the above code is the call to the method, which specifies that our neural network’s input type is convolutional, with 32×32 images having 3 colors.
- To start training the convolutional neural network you just created, just call its method and pass the iterator object to it.
In this tutorial, you’ll learn how to use Java and DeepLearning4J(DL4J) to create a convolutional neural network that can classify CIFAR-10 images.
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- When Unanimous AI developed UNU in 2015, the goal was to create artificial intelligence (AI) systems that “keep people in the loop,” amplifying human intelligence instead of replacing it.
- Unanimous AI’s March Madness bracket was able to beat all but three percent of ESPN brackets across the country on the first day of the tournament.
- The technology makes use of the collective intelligence of people — combining “knowledge, insights, and intuitions,” as Unanimous AI puts it — to develop a kind of artificial intelligence that’s inherently human.
- As Unanimous AI explains, “We empower people to act as ‘data processors’ that come together online and form an intelligent system, connected by AI algorithms.
- One day, perhaps we’ll be able to combine the intelligence of Watson with that of a swarm of medical professionals to improve healthcare, or combine the insights of an investment-making AI with a swarm of finance experts.
The collective intelligence is killing it on ESPN.
Continue reading “March Madness: A Swarm Intelligence Is Predicting the Future”
- In February, Twitter announced it had 319 million monthly active users worldwide, or just slightly under the number of every person in the United States.But of those 319 million, as many as 48 million aren’t actually real, according to a study conducted by researchers from the University of Southern California: They’re just software programs, designed to do everything a normal person on Twitter would do, including following other accounts and liking and retweeting certain messages.Those accounts, called “bots,” can range from accounts dedicated to alerting their followers about emergencies to political advocates intended to boost the numbers of a programmer’s preferred candidate.
- “Many bot accounts are extremely beneficial, like those that automatically alert people of natural disasters … or from customer service points of view,” a Twitter spokesperson told CNBC.However, there are also plenty of fake accounts.
- Because a person’s number of Twitter followers is often seen as indicative of how popular and powerful that person is, there are services that allow people to buy followers, and quite often those services use bots as part of their service.Twitter has acknowledged the existence of bots in the past and has attempted to crack down on them, suspending accounts they believe are not human.
- However, as CNBC reports, the USC study goes far beyond what Twitter itself has claimed about the number of bots on its platform.
- In February, Twitter estimated in a SEC filing that up to 8.5 percent of its users were not human, while the USC study’s authors say even its estimate of 15 percent is “conservative.”
The study, from researchers at the University of Southern California, found that anywhere between nine and 15 percent of active users on the social media site are automated software that can like, retweet and follow just like accounts managed by humans.
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- That began to change with the release of a number of open-source machine learning frameworks like Theano, Spark ML, Microsoft’s CNTK, and Google’s TensorFlow.
- Among them, TensorFlow stands out for its powerful, yet accessible, functionality, coupled with the stunning growth of its user base.
- With this week’s release of TensorFlow 1.0, Google has pushed the frontiers of machine learning further in a number of directions.
- In an effort to make TensorFlow a more-general machine learning framework, Google has added both built-in Estimator functionality, and support for a number of more traditional machine learning algorithms including K-means, SVM (Support Vector Machines), and Random Forest.
- While there are certainly other frameworks like SparkML that support those tools, having a solution that can combine them with neural networks makes TensorFlow a great option for hybrid problems.
Google gives everyone machine learning superpowers with TensorFlow 1.0
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- But one thing that is needed for searching for a cure for cancer, even when using AI, is data.
- Although there are various treatments available that are improving all the time, there is still no cure for cancer.
- To make data more available for cancer research purposes, three things need to happen.
- More standardization and shared access are required to make the best use of this data.
- With all of the data to hand, researchers estimate that by 2025, as many as 2 billion human genomes could be sequenced.
Cancer is a devastating disease and statistics now suggest that nearly one in every two people worldwide will develop cancer at some point in their lives.
Continue reading “Is Artificial Intelligence the Answer to Finding a Cure for Cancer?”