New Product breakthroughs with recent advances in deep learning and future business opportunities
Even though artificial intelligence was introduced in the early fifties, It has only been made attainable very recently thanks to the recent advances in deep learning, artificial neural networks, increased performance of transistors, latest advancements in GPUs, TPUs, and CPUs.
Before going over the role of artificial intelligence (AI) and machine learning (ML) in Google and Apple products which were highlighted in their 2017 keynotes, I would like to shed some light on the differences between AI and ML, also briefly explain how deep learning is combining both AI and ML and how they are opening up new business opportunities.
Picture 1: evolution of deep learning (Source )
Artificial intelligence is something that is just machine tuned to cater our requirements; Examples include PIC microcontroller based room temperature measurement and the currently popular facial recognition features in the latest ios and android smartphones. Therefore, a classic AI can perform specific tasks as instructed but it is not capable of learning on its own and program.
Machine learning is the processing of real world data using algorithms, analyzing the results and predicting the output data using multiple iterations. AI can be used to further refine or generate new algorithms to get better results, and machine learning programs can align themselves to these algorithms to get much finer granularity in results.
Deep Learning, however, uses ML and AI together to break down tasks, analyze each subtask and uses this information to solve new set of problems
One example of deep learning is the artificial neural network (ANN) which is based on an idea of how our human brain works. ANN finds common patterns from given data and predicts the best result.
With this I would like to go over keynotes of Google and Apple regarding the use of AI and ML in their new products:
Google IO 2017 Keynote, May 2017:
Sunder Pichai CEO started the keynote with “mobile first to AI first” which clearly shouts out to the world that Google is now moving towards AI.
Google has chosen three major fields where they are extensively involving themselves in research and development of AI
- Voice: Google quoted that their products are now better at voice recognition, with speech recognition error rate dropping down from 8.5 % to 4.7 % over the past year. They launched a new product “Google assist – conversation with Google to get things done in your world”. Major features of this product is speech recognition and text patterning
- Vision: Google quoted that the image recognition algorithms used in their products are better than the human capacity! Their new product “Google Lens” has a vision sensing capability that can understand what you are looking at and helps you by providing information related to that.
- Data Storage: with advancements in AI, Google has started to redesign their hardware architecture of Tenser Processing Unit (TPU). They launched their next generation of TPUs called “cloud TPUs” which is optimized for training and inference. A single cloud TPU is capable of performing 180 trillion floating point instructions, which can definitely one of the major breakthroughs to take AI to next level
Google also working on reinforcement learning approach of neural network to bring the AI across wide range of disciplines
- Healthcare – used for breast cancer diagnosis,
- Biology-Accuracy of DNA sequencing to detect genetic disease
- Chemistry – to predict property of molecules
Apple WWDC 2017, June 2017:
- Tim Cook CEO announced 6 new products, and each product has made use of AI wherever it is possible
- Apple TV – using Amazon cloud to bring more channels
- Apple Watch – Have voice intelligence, AI to track your daily routines
- 7th generation MAC supercomputer- high configuration GPUs, up to 18 core Xeon CPUs, mainly designed for use in the real-time 3D rendering, ML, complex AI simulations and analysis
- IOs 11 –
- Siri for understanding voice context
- Apple MAPS with interesting AI features
- machine learning tool (CML) provides deep neural network, recurrent neural networks
- Augment reality tools for fast and stable motion tracking etc
- IPAD- 12 core GPUs to support many AI applications
- Home pod- It has A8 chips which support real-time acoustic modeling, audio beam forming
Both Apple and Google are trying to take advantage of deep learning in their products. This definitely has a huge impact on human lifestyles. We may expect much more AI-based products from them in near future.
What can we expect in future?
Based on these keynotes, I personally feel that there are some business challenges for tech companies to deal with before bringing AI into many other fields, some of them are
- Provision of value to customers even if deep learning fails in some cases. Need for a plan B in case the whole idea fails
- Since AI and ML are complex fields, only a handful of experts are currently working on them, Companies and Universities need to educate and train people and create resources
- Need to focus constantly on designing better GPUs, TPUs and multicore CPUs in order the explore AI opportunities in various discipline
Despite these challenges, current breakthroughs in chip design industries are now building more and more power-packed hardware platforms such as Nvidia Volta GPUs, Intel K-Series i-core processors, Googles next generation TPUs and Apple workstations. All of these devices are now capable of taking on the next level complex AI challenges and refine the existing AI applications.
I feel confident that AI can answer many real-world problems, such as planning a city, constructing buildings, designing unmanned air vehicles, developing machines which can recognize human emotions. The list simply grows! This will opens up new business opportunities and will become a game changer for world economy in coming days
 Google IO 2017 Keynote, May 2017: https://goo.gl/4vBJZT
 Apple WWDC 2017, June 2017 https://www.apple.com/apple-events/june-2017/
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