The Coronavirus crisis continues with companies and governments collaborating via modeling and tracking data to manage the pandemic.
AI is helping by developing antibodies for COVID-19, repurposing drugs and creating structure predictions of the virus such as the Alpha System from Google’s Deep Mind.
Amazon is boosting online shopping experiences through machine-learning tools to predict customer queries alongside computer vision. Personalized shopping experience is vital for Amazon customers and with Deep North developing an analytics platform for retailer insights, the future of retail looks bright.
What about women leading the AI Movement in 21st Century?
Yes, women are taking charge of artificial intelligence. From Dr. Vivienne Ming and Jana Eggers, women are defining how AI will influence human society in this digital era.
These and more insights on the weekly AI updates
Driving Value through AI Investments
According to a Gartner survey, 48% of global CIOs will deploy AI by the end of 2020. However, despite all the optimism around AI and ML, I am still a little skeptical.
Here are some trends that may be going unnoticed at the moment but will have big long-term impacts:
With ML solutions¹ becoming more demanding in nature, the number of CPUs and RAM are no longer the only way to speed up or scale. More algorithms are being optimized for specific hardware than ever before — be it GPUs, TPUs, or “Wafer Scale Engines.” This shift towards more specialized hardware to solve AI/ML problems will accelerate.
There will be a gradual shift in the focus on data privacy towards privacy implications on ML models. A lot of emphasis has been placed on how & what data we gather and how we use it.
But ML models are not true black boxes. It is possible to infer the model inputs based on outputs over time. This leads to privacy leakage.
Organizations aspiring to drive value through their #AIinvestments need to revisit the implications on their data pipelines.
The women defining the 21st-century AI Movement.
The focus now is to highlight more top minds in AI but to specifically focus on women who are defining the 21st century’s artificial intelligence movement.
1. Dr. Vivienne Ming @neuraltheory: Founder of Socos Labs
“Dr. Ming is a theoretical neuroscientist, entrepreneur, and author. She co-founded her fifth company, Socos Labs, which explores the future of human potential. In her own time, she has created AI systems² to help treat her diabetic son, predict manic episodes in bipolar sufferers weeks in advance, and reunite orphan refugees with extended family members.”
2. Jana Eggers @NaraLogics: Chief Executive Officer at Nara Logics
“Nara Logics’ works from the approach that big data only matters if you’re able to build a contextual understanding around any defined dataset. Their desire to discover specified, contextualized data matching to help businesses in virtually every industry is moving AI in a direction to be more actionably influential to every American.”
Her passions include her work with teams to define and deliver products customers love, algorithms and their intelligence, and inspiring teams to do more than they thought possible.
Modeling and Data Tracking for Coronavirus Crisis
Executives from Amazon, Google, Microsoft, Apple, and Facebook met officials at Downing Street in mid March to discuss their role in the coronavirus crisis³. One of the things discussed was their role in “modeling and tracking data”.
In similar meetings at the White House, meanwhile, companies were asked how they could use artificial intelligence.
A World Health Organization report last month said AI and big data were a key part of China’s response to the virus.
Facebook is already working with researchers at Harvard University’s School of Public Health and the National Tsing Hua University, in Taiwan, sharing anonymized data about people’s movements and high-resolution population density maps, which help them forecast the spread of the virus.
AI could be used in three ways in the current crisis, according to chief executive Prof. Andrew Hopkins:
– To rapidly develop antibodies and vaccines for the Covid-19 virus
– To scan through existing drugs to see if any could be repurposed
– To design a drug to fight both the current and future coronavirus outbreaks
Google-owned AI company Deep Mind, meanwhile, has used its Alpha Fold system to release structure predictions of several proteins associated with the virus.
AI Technology Vendors
While artificial intelligence-powered technologies are now appearing in many digital services we interact with on a daily basis, and often neglected truth is that few companies are actually building the underlying AI technology.
A good example of this is facial recognition technology, which is exceptionally complex to build and requires millions upon millions of facial images to train the machine learning models.
Consider all of the facial recognition⁴-based authentication & verification components of all the different services you use. Each service did not reinvent the wheel when making facial recognition available in their service; instead, they integrated with an AI technology provider.
An obvious case of this is iOS services that have integrated Face ID, for example, to quickly log into your bank account. Less obvious cases are perhaps where you are asked to verify your identity by uploading images of your face and your identity document to a cloud service for verification.
We are hearing that the governments using facial recognition in public forums to identify individuals in a crowd, but it is not as though each government is building their own facial recognition technology. They are purchasing technology from an AI technology vendor.
AI and ML for Customer Experiences
Amazon is using AI and machine learning to predict context from customers’ queries.
In a preprint paper accepted to the ACM SIGIR Conference on Human Information Interaction and Retrieval scheduled to take place this month, Amazon researchers describe a system that predicts activities like “running” from queries like “Adidas men’s pants.” It could help to improve the quality of search results on Amazon.com, which could enhance the overall Amazon shopping experience.
As @AdrianBoteanu, contributing author and Amazon Search customer experience applied scientist, explains in a blog post, most product discovery algorithms look for correlations between queries and products⁵. By contrast, the researchers’ AI identifies the best matches depending on the context of use.
To train the system, the team assembled a list of 173 context-of-use categories divided into 112 activities and 61 audiences based on common product queries. They used standard reference texts to create aliases for the terms they used to denote the categories, and then they scoured a corpus relating millions of products to query strings for reviews for the category terms plus their aliases.
Amazon using Computer Vision to boost Shopping Experiences
Amazon and others have raised awareness of how the in-store shopping experience can be sped up using computer vision⁶ to let a person pay for and take away items without ever interacting with a cashier, human or otherwise.
Today, a startup is announcing funding for its own take on how to use AI-based video detection to get more insights out of the retail experience.
DeepNorth¹⁰, which has built an analytics platform that builds insights for retailers based on the videos from the CCTV and other cameras that those retailers already use, is today announcing that it has raised $25.7 million in funding, a Series A round that it plans to use to continue expanding its platform.
Deep North’s AI currently measures such parameters as daily entries and exits; occupancy; queue times; conversions and heat maps — a list and product roadmap that it’s planning to continue growing with this latest investment.
It says that using cameras to build its insights is more accurate and scalable than current solutions that include devices like beacons, RFID tags, mobile networks, smartphone tracking, and shopping data. A typical installation takes a weekend to do.
The funding is being led by London VC Celeres Investments, with participation also from Engage, AI List Capital and others.
Data Science is in the midst of a Revolution
While the mathematical tools to make predictions from data have existed for centuries, and the algorithmic ones for several decades, all have required humans to manage the data inputs and interpret/iterate on the outputs.
Artificial Intelligence (AI) has the potential to change all of that.
Predictions that once required months to generate and required frequently manual tweaks could instead be handled by powerful technologies that operate near-autonomously. Innovative companies understand the enormous potential of this technology, and many are attempting to migrate to an AI-centric operational model.
@MarkCuban is one of many investors who foresees AI’s unparalleled impact. He’s said, “As big as PCs were an impact, as big as the internet was, AI is just going to dwarf it. And if you don’t understand it, you’re going to fall behind. Particularly if you run a business.”
Indeed, strong evidence supports Cuban’s predictions — it is estimated that AI will add⁷ a staggering $13 trillion to the global economy over the next decade
Opening the Blackbox of Machine Learning
Google today made available Neural Tangents, an open-source software library written in JAX, a system for high-performance machine learning research. It’s intended to help build AI models of variable width simultaneously, which Google says could allow “unprecedented” insight into the models’ behavior and “help … open the blackbox” of machine learning.
As Google senior research scientist Samuel S. Schoenholz and research engineer Roman Novak explain in a blog post, one of the key insights enabling progress in AI research⁸ is that increasing the width of models results in more regular behavior and makes them easier to understand.
By way of refresher, all neural network models contain neurons arranged in interconnected layers that transmit signals from input data and slowly adjust the synaptic strength of each connection.
Machine learning models that are allowed to become infinitely wide tend to converge to another, simpler class of models called Gaussian processes. In this limit, complicated phenomena boil down to simple linear algebra equations, which can be used as a lens to study AI.
But deriving the infinite-width limit of a finite model requires mathematical expertise and has to be worked out separately for each architecture.
AI and ML Expanding Robotics
Consider a company that needs to summarize long-winded, handwritten notes. AI algorithms that perform character recognition and natural language processing could read the cursive and summarize the text, before a software robot inputs the text into, say, a website.
“When paired with robotic process automation, AI significantly expands the number and types of tasks that software robots can perform,” says Tom Davenport, a professor who studies information technology and management at Babson College.
The latest version of Ui Path’s software includes a range of off-the-shelf machine learning tools. It is also now possible for users to add their own machine learning models to a robotic process.
With all the AI hype, it’s notable that so little has found its way into modern offices. But the automation that is there, which simply repeats a person’s clicking and typing, is still useful.
The technology is mostly used by banks, telcos, insurers, and other companies with legacy systems; market researcher Gartner estimates the industry generated roughly $1.3 billion in revenue in 2019.
Data Privacy Violations and AI based Standards
We are now beset by data breaches and data privacy scandals, and regulators around the world have responded with data regulations.
GDPR is the current role model, but we expect a global group of regulators to expand the rules to cover AI more broadly and set the standard on how to manage it.
The UK ICO released a draft but a detailed guide on auditing AI. The EU is developing one as well. Interestingly, their approach is very similar to that of the Basel standards: specific AI risks should be explicitly managed. This will lead to the emergence of professional AI risk managers.
Today most data regulations around the world have focused on data privacy. Data privacy is a subset of data protection. GDPR is more than just privacy. Data protection is a subset of AI regulation. The latter covers algorithm/model development as well.
Given the privacy violations in recent times, we can see GDPR as a Basel standard equivalent to the data world. And we can see the European Data Protection Supervisor (EDPS) as the BCBS for data privacy. Potentially, a more global group will emerge as more countries enact data protection laws.
Tasks Automation using Integrated Machine Learning Algorithms
Nearly every company has processes suited for machine learning, which is really just a way of teaching computers to recognize patterns and make decisions based on those patterns.
Is that a dog on the road in front of me? Apply the brakes. Is that a tumor on that X-ray? Alert the doctor.
What only insiders generally know is that data scientists, once hired, spend more time building and maintaining the tools for AI systems then do the building the systems themselves.
A recent survey of 500 companies by the firm Algorithmia¹¹ found that expensive teams spend less than a quarter of their time training and iterating machine-learning models.
Now, though, new tools are emerging to ease the technological innovation.
Unified platforms that bring the work of collecting, labeling and feeding data into supervised learning models or that help build the models themselves, promise to standardize workflows in the way that Salesforce and Hubspot has for managing customer relationships.
Some of these platforms automate complex tasks using integrated machine learning algorithms, making the work easier still. This frees up data scientists to spend time building the actual structures they were hired to create and puts AI within reach of even small- and medium-sized companies.