Artificial Intelligence in today’s MNCs
“Our intelligence is what makes us human, and AI is an extension of that quality.”
Yann LeCun
Professor, New York University
Introduction
Hundreds of years had passed since early attempts at describing human thinking by philosophers, and the formal founding of Artificial Intelligence (AI) in 1956; Yet, significant growth in the field since its founding has been observed only in recent times. With AI now being able to trick judges by passing the Turing test, scientists now plan to develop an updated version of the test. For AI to come close to clearing the test developed by Alan Turing in 1950 which had stayed relevant for years, it is evident that the growth of Artificial Intelligence is extensive.
Just like in any other field of technology, applications and implementation is rapid in companies and organizations that can profit from the field of AI. Artificial Intelligence and it’s subgroup Machine Learning has thus been crucial for the innovation and development of various Multinational Corporations around the globe.
What is Artificial Intelligence ?
A living organism or ‘forms of life’ show a type of intelligence called Natural Intelligence. It is a system of control present in organisms and are a biological development. Human intelligence is thus natural intelligence, and its one of the most sophisticated and developed system of control on the Earth. We have been trying to understand neuroscience in various organisms, and the understanding of network of neurons in our body has given us a lot to know about human intelligence. Artificial Intelligence is simply an artificially made system or model that replicates natural intelligence as accurately as possible, to be able to come up with a some desirable output (that a naturally intelligent system would provide) when some input is given. It is made to learn from previous tasks just like what we observe in natural intelligence. Various concepts like digital assistants, self-driving and assisting cars, image and object recognition, improvement of customer experience, recommendation models, etc. all have artificial intelligence as their root.
How are Machine Learning and Artificial Intelligence different ?
AI is cannot be interchangeably referred as Machine Learning (ML). AI is a much bigger field containing subfields like ML and Deep Learning (DL). Machine Learning includes operations research, statistical data analysis, neural networks and physics to find patterns in data without the need of being explicitly programmed.
Companies and their interest in AI
The priority of every business is simple, maximize profits for its owners or stakeholders while maintaining corporate social responsibility. Let’s focus on the part about profits. Declining profits are obviously a concern for any business, but decreasing investment, innovation and long-term shareholder value is even more worrisome. This is because, a fall in investments corresponds to erosion in growth and a freeze of resources in a highly disruptive environment. For instance, in Manufacturing business investment growth has declined from 14.8 percent in 2012 to -5.2 percent in 2016 in the United States and from 5.9 percent in 2012 to -6.6 percent in 2016 in the United Kingdom. This is where AI can help in economic growth, and corporate profitability. By allowing machines to extend human capabilities by sensing, comprehending, acting and learning, it lets us achieve much more.
Nvidia
Nvidia Corporation is an American company, highly known for their graphics processing units, as well as system-on-a-chip units (SoCs) for mobile computing and automating. The company is an avid participant in the research on artificial intelligence and its methods of use. For instance, by implementing convolutional neural networks (CNNs), generative adversarial networks (GANs) and autoencoders, in face swapping and image denoising to character locomotion, facial animation and texture creation, the company has made progress in the field of media and entertainment.
Their NVIDIA DRIVE platform can simultaneously process data from up to 16 sensors, constantly collecting vital data for building a robust autonomous driving training library. A startup Perceptive Automata is using human behavior data with NVIDIA DRIVE to predict pedestrian movements. For more refer this.
High Performance Computing (HPC) leverages GPU-powered parallel processing across multiple compute nodes to run advanced, large-scale application programs efficiently, reliably, and quickly.
An example would be the Laser Interferometer Gravitational-wave Observatory (LIGO) being able to detect gravitational waves millions of light years away in real-time with the help of Nvidia Tesla GPUs and concepts of deep learning along with computing resources like XSEDE, Blue Waters, and Open Science Grid. The AI inference method improved performance by a factor of 100X, and the GPU improved the performance of the AI inference by another 50X. For more info refer this.
Another application of AI by Nvidia is the Broadcast app which offers three AI-powered features for streaming:
- Noise Removal: remove background noise from your microphone feed — be it a dog barking or the doorbell ringing. The AI network can even be used on incoming audio feeds to mute that one keyboard-mashing friend who won’t turn on push-to-talk.
- Virtual Background: remove the background of your webcam feed and replace it with game footage, a replacement image, or even a subtle blur.
- Auto Frame: zooms in on you and uses AI to track your head movements, keeping you at the center of the action even as you shift from side to side. It’s like having your own cameraperson.
Tesla
One of the leading electric vehicle and clean energy company in the world, Tesla has been an active competitor in the field of autonomous driving.
Tesla has taken excellent use of AI and Big Data for expanding its customer base. The firm has made use of existing customer databases for its data analytics using it to comprehend customer requirements and regularly updating their systems accordingly.
In the case of Artificial Intelligence, Tesla has leveraged it to focus on mainly 2 areas: All electric propulsion and autonomous driving. Initially, Tesla had collaborated wit Nvidia to optimize its AI integrated chips. Later, the company ended their collaboration to create their own chips. Tesla confirmed that its performance has largely boosted owing to the heavy optimizations in the AI chip. A massive number of transistors have been used — 6 billion — which constitute the processing circuitry for each of Tesla’s chips.
These AI chips have been optimized to run at 2 GHz and perform 36 trillion operations per second, achieving this level of performance by dismissing all generic functions and channeling the focus on only the important ones.
All Tesla vehicles — whether or not they are Autopilot enabled — send data directly to the cloud. The data is used to generate highly data-dense maps showing everything from the average increase in traffic speed over a stretch of road, to the location of hazards which cause drivers to take action. Machine learning in the cloud takes care of educating the entire fleet, while at an individual car level, edge computing decides what action the car needs to take right now. A third level of decision-making also exists, with cars able to form networks with other Tesla vehicles nearby in order to share local information and insights.
Apple
Though not as active as the competition (Google, yes.) in routing the technology of AI, the consumer electronics giant still has dedicated hardware for machine learning tasks in most of the devices it ships. In recent years Apple has engaged more with the AI community and has machine learning powering numerous features in its’s devices.
ML is used to optimize device battery life and charging based on usage habits and also to make app recommendations. Apple’s well known voice assistant Siri is driven by several aspects of machine learning, from speech recognition to attempts at providing useful answers.
Features like accidental palm touch while drawing on the iPad is cancelled out using machine learning.
The Photos app ability to automatically sort pictures into pre-made galleries and provide accurate picture results based on input given in the search field is all driven by machine learning.
Phones have long included image signal processors (ISP) for improving the quality of photos digitally and in real time, but Apple accelerated the process in 2018 by making the ISP in the iPhone work closely with the Neural Engine, the company’s recently added machine learning-focused processor.
Features like language-translation, on-device dictation, and recent new features around health, like sleep and hand washing, implementing saliency in photographs like portrait mode, keyboard predictions, are all rooted by concepts of machine learning and built into the core of the device.
Apple performs machine learning tasks locally on the device, on hardware like the Apple Neural Engine (ANE) or on the company’s custom-designed GPUs. This local approach in contrast to many other companies using massive data collection engines is due to Apple’s commitment to limit its data collection, thus preserving the privacy of a user.
Conclusion
Above were just a few examples of major companies using AI to improve their reach and innovate, to make products and services that implement Artificial Intelligence at its core to solve some real world problem or improve user experience. AI has certainly affected the way MNCs operate by replacing a lot of positions of intensive work that are prone to human error. The ability to train an artificial model to perform tasks that would be almost impossible to program otherwise has allowed the development of consumer services to grow more and more. Though AI as a technology has offered many solutions with its ability to mimic human capabilities, it has also made many positions of work obsolete for humans. We’re yet to see the complete impact of an extensively AI integrated environment around us, but for now, the technology looks as promising as ever.
“We have seen AI providing conversation and comfort to the lonely; we have also seen AI engaging in racial discrimination. Yet the biggest harm that AI is likely to do to individuals in the short term is job displacement, as the amount of work we can automate with AI is vastly larger than before. As leaders, it is incumbent on all of us to make sure we are building a world in which every individual has an opportunity to thrive.”
Andrew Ng
Co-founder and lead of Google Brain
