Interesting stuff of AI, Machine learning, and Deep Learning 2017–09 #2
A List of Chip/IP for Deep Learning (keep updating)
1. AI and the cloud crunch the numbers for Huawei’s new mobile chip
The brains of most phones are a CPU and a GPU for processing information and providing graphical grunt, respectively. But with its next phones, Huawei is looking to cram a third type of processor in there: what it calls a Neural Processing Unit (NPU). The company’s new flagship chip, the Kirin 970, will include a dedicated AI processor, and offload some of the work to the cloud.
2. Sequoia, IDG to Invest in China Bitcoin Mining Giant
Sequoia Capital and IDG Capital are investing in Beijing-based Bitmain Technologies Ltd., the world’s largest bitcoin mining organization, according to people familiar with the matter.
3. Becoming a 10x Data Scientist
Recently I gave a talk at PyData Seattle about how to ramp up your data science skills by borrowing tips and tricks from the developer community. These suggestions will help you become a more proficient data scientist who is loved by your team members and stakeholders.
4. Intro to The Data Science Behind EEG-Based Neurobiofeedback
The Neurobiofeedback machine gained popularity for its non-invasive and quantitative approach to behavior regulation, but its legitimacy remains in question by pediatricians, therapists, and other professionals. In academic-sounding terms, this machine (which I’ll be abbreviating as NBF from now on) is built on the concept of feedback therapy, which exploits our ability to exert and/or regain control over physiological aspects in our body.
5. Facebook and Microsoft introduce new open ecosystem for interchangeable AI frameworks
Facebook and Microsoft announced ONNX, the Open Neural Network Exchange this morning in respective blog posts. The Exchange makes it easier for machine learning developers to convert models between PyTorch and Caffe2 to reduce the lag time between research and productization.
6. New AI can guess whether you’re gay or straight from a photograph
An algorithm deduced the sexuality of people on a dating site with up to 91% accuracy, raising tricky ethical questions. Artificial intelligence can accurately guess whether people are gay or straight based on photos of their faces, according to new research that suggests machines can have significantly better “gaydar” than humans.
7. MEET MICHELANGELO: UBER’S MACHINE LEARNING PLATFORM
Uber Engineering is committed to developing technologies that create seamless, impactful experiences for our customers. We are increasingly investing in artificial intelligence (AI) and machine learning (ML) to fulfill this vision. At Uber, our contribution to this space is Michelangelo, an internal ML-as-a-service platform that democratizes machine learning and makes scaling AI to meet the needs of business as easy as requesting a ride.
8. Introducing Pytorch for fast.ai
The next fast.ai courses will be based nearly entirely on a new framework we have developed, built on Pytorch. Pytorch is a different kind of deep learning library (dynamic, rather than static), which has been adopted by many (if not most) of the researchers that we most respect, and in a recent Kaggle competition was used by nearly all of the top 10 finishers.
9. The new rules of Build vs. Buy in an AI-first world
Since the field of AI and specifically computer vision has evolved to a point of being accurate and scalable enough to be useful in real-world applications, enterprise businesses have begun to realize the tremendous efficiency and revenue gains that are possible. These types of process efficiency gains are hard to ignore, and as a result, enterprise businesses are seriously evaluating the spectrum of possible AI strategies for the first time. The problem for many businesses is that it’s difficult to make an informed decision when there are so many possible solutions. There is the traditional ‘build in-house’ route dominated by open-source toolkits like Kaffe, Theano, and Tensorflow, or the full-service consulting approach led by IBM, with the artificial intelligence as a service (AIaaS) approach somewhere in-between.
