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The state of artificial intelligence today

Today, AI can process millions of points of disparate data, find unseen patterns and correlations, and deliver powerful, prescriptive insights to augment human decisions.

AI can even replicate human thinking – but at great speed and scale – for greater efficiency and productivity.

According to the AI Index Report from Stanford University's Institute for Human-Centered Artificial Intelligence (HAI), 58% of large companies say they've adopted AI in at least one function or business unit in 2019 – up from 47% in 2018.

The Economics of AI Today

Every day we hear claims that Artificial Intelligence (AI) systems are about to transform the economy, creating mass unemployment and vast monopolies.

It should therefore not come as a surprise that AI systems able to behave appropriately in a growing number of situations - from driving cars to detecting tumours in medical scans - have caught their attention.

That kind of customization would not be possible without a machine learning system (a type of AI) that predicts automatically what products might be of interest to the individual customer based on data about her behavior and other customers who are similar to her.

At the same time, and contrary to an standard assumption in economics that new technologies always increase labor demand through augmentation, the task-based model recognizes that the net effect of new technology on labor demand could be negative.

In contrast to tangible assets like machines or buildings, intangibles are hard to protect, imitate and sell, and their creation often involves costly experiments and learning by doing (much more on this subject here).

According to a 2018 paper by Erik Brynjolfsson and colleagues, the need to accumulate these intangibles across the economy could explain why advances in AI are taking so long to result in productivity growth or drastic changes in labor demand.

Several papers presented in Toronto this year explored these questions empirically: Think of a sector like health: the nature of production in this industry, as well as the availability of data, the scope to change business processes and its industrial structure (including levels of competition and entrepreneurship), are completely different from, say, finance or advertising.

Similarly, the firms adopting AI systems look like black boxes to economists adopting a macro perspective: AI intangibles are after all a broad category of business investments including experiments with various processes, practices and new businesses and organizational models.

As they do this, they are also incorporating into the Economics of AI some of the complex factors that come into play when firms deploy AI systems that do not just increase the supply of predictions, but also reshape the environment where other actors (employees, consumers, competitors, the AI systems themselves) make decisions, leading to strategic behaviors and unintended consequences.

Several papers mentioned above explored new data sources and methods along these lines, for example using big datasets from LinkedIn and Uber, and online experiments to test how UberX drivers react to informational nudges.

Although these methods open up new analytical opportunities, they also raise challenges around reproducibility, particularly when the research relies on proprietary datasets that cannot be shared with other researchers (with the added risk of publication bias if data owners are able to control what findings are released), and ethics, for example around consent for participation in online experiments.

These factors could reduce AI's impact on productivity (making it mediocre and therefore predominantly labor displacing), increase the need to invest in new complements such as AI supervision and moderation, hinder trade in potentially dodgy AI products and services, and have important distributional implications, for example through algorithmic discrimination of vulnerable groups.

Macro research on AI should start to consider explicitly these complex aspects of AI adoption and impact, rather than hiding them in the black box of AI-complementing intangible investments and/or assuming that they are somehow exogenous to AI deployment.

For example, the fact that researchers in academia increasingly need to collaborate with the private sector to access the data and compute required to train state of the art AI systems could skew this research or reduce its public value.

Meanwhile, the diffusion of AI research through open channels creates important challenges for regulators who need to monitor compliance in an environment where adopting dangerous AI technologies is as simple as downloading and installing some software from GitHub.

This model could be operationalized using data from open and web sources and initiatives to measure AI progress from the Electronic Frontier Foundation (EFF) and the Papers with Code project in order to study the structure, composition and productivity of the AI industry, and how it supplies AI technologies and knowledge to other sectors.

Lack of diversity in the AI research workforce, and the increasing influence of the private sector in setting AI research (and ethical) agendas as part of the industrialization of AI research suggest that this could be a problem, but the evidence base is lacking.

These important questions were largely absent from the debate in Toronto, yet economists need to formalize and operationalize models of the distributional impacts of AI and its externalities in order to inform policies to ensure that its economic benefits are widely shared and reduce the risk of a public backlash against it.

Prediction machines not only increase the amount of decisions we are able to make based on AI recommendations, but also the amount of decisions that we need to make, as participants in the economy and as a society, about what AI technologies to develop, where to adopt them and how, and how to manage their impacts.

The 3 Best Artificial Intelligence Stocks to Buy Now for Your Portfolio

As demand for storage and memory grows across AI applications -- including cloud data centers, self-driving cars, and 5G phones -- through the 2020s, the storage and memory industry should grow along with it.

The fact that the company dominates the market for supplying discrete graphics processing units (GPUs) for computer gaming is simply a great side benefit.  Within less than a decade, management has masterfully transformed NVIDIA into a major AI player.

It involves a machine or device applying what it's learned in its training to new data.)  NVIDIA has been profiting mightily from data center AI applications for the past few years, and is in the earlier stages of profiting from many burgeoning AI-driven growth trends that promise to be huge, including smart homes, driverless vehicles, smart cities, and drones.

Combine that fact with a founder-CEO who's shrewd, agile, and determined to win and the stock seems poised to keep on winning. And pay no attention to Wall Street's relatively modest 10.7% annualized earnings growth projection for the next five years.

Saleforce's Einstein AI software is integrated into its offerings so that even small businesses can use the technology to make individualized product recommendations, create an email marketing campaign with personalized messages, implement chatbots for website help, and provide guidance to service representatives working on solving customer problems.

Salesforce President and Chief Product Officer Bret Taylor said recently that Einstein is 'really our main differentiator,' a rather startling statement from a company that's been growing revenue at a 26% compound annual growth rate.  Of the 32 million orders made on the company's platform during Cyber Week last year, over 10% were  driven by Einstein recommendations, and the software is doing over 10 billion predictions per day.

AI is critical to the rapidly growing company's expectation that it'll double its revenue in the next four years, and Salesforce stock gives investors an opportunity to profit as AI technology permeates business processes around the globe.