Brigadoon Weekend
Emerging issues shaping commerce and culture.
September 26, 2020
Artificial Intelligence Only.
ARTIFICIAL INTELLIGENCE DEEP DIVE SIX
Vivienne Ming: ‘The professional class is about to be blindsided by AI’: Since changing gender, the Tedx Talk star has been on a mission to show how tech can improve lives. Ming has developed bots that trawl the web looking for high-tech programmers who may not even have a degree yet are doing great work. She’s also used AI to calculate the “tax on being different”, calculating, for example, that in the technology sector, a Latino worker needs about six years’ more education than a white worker to be considered for the same job — something, given the cost of US tertiary education, that can amount to $500,000 or more.
FT
The new business of AI (and how it’s different from traditional software): At a technical level, artificial intelligence seems to be the future of software. AI is showing remarkable progress on a range of difficult computer science problems, and the job of software developers – who now work with data as much as source code – is changing fundamentally in the process. Many AI companies (and investors) are betting that this relationship will extend beyond just technology – that AI businesses will resemble traditional software companies as well. Based on our experience working with AI companies, we’re not so sure.
Martin Casado + Matt Bornstein
Why deep-learning AIs are so easy to fool: Artificial-intelligence researchers are trying to fix the flaws of neural networks.
Nature
The trouble with data: Most AI algorithms today are trained with input-output pairs: “Here are 10,000 MRI scans, and this is what a tumor looks like.” Your algorithm can then do only one thing: identify tumors in scans. But it can do that one thing really well, at scale, and with a high degree of accuracy. Patient data is sensitive, siloed, and messy, and begs some basic questions about access and accuracy for algorithm training.
CB Insights
Training a single AI model can emit as much carbon as five cars in their lifetimes: Deep learning has a terrible carbon footprint. Researchers at the University of Massachusetts, Amherst, performed a life cycle assessment for training several common large AI models. They found that the process can emit more than 626,000 pounds of carbon dioxide equivalent—nearly five times the lifetime emissions of the average American car (and that includes the manufacture of the car itself).
MIT Technology Review
Can artificial intelligence in the energy sector help solve the climate crisis? Artificial intelligence conjures fears of job loss and privacy concerns — not to mention sci-fi dystopias. But machine learning can also help us save energy and make renewables better. To fulfill the Paris Agreement, we will have to virtually eliminate fossil-fueled energy from all sectors of the economy. This will mean networking decentralized, fluctuating renewable power generation with consumers that automatically adjust to minimize waste and balance the entire system.
DW
ROSS RANTS
The future is loudspeakers or why there is no Apple equivalent in Russia
When the world is changing rapidly, it is easy to be left behind when you make the wrong strategic decision.
Consider Moscow in 1917.
While the rest of the world was investing in telephone networks, leaders of the Soviet Union – fearing the decentralized nature of phone networks and thinking the top-down management of czars past was the future - made strategic a decision that has impacted the nation for over a century.
The leaders opted for a nation-wide system of loudspeakers.
Wrapped in misplaced strategic thinking, the centralized government now believed they would have the proper tool to mass broadcast directly to the people while inhibiting their citizens' ability to speak to each other.
Their poor strategic decision prevented efficient collaboration between workers and certainly hindered Russia's economic growth and innovation.
The past is often the just past.
What worked in the past probably won't serve you well in the future - especially if you are keen to power a nation forward.
Rise of the Chief Data Officer (CDO)
A new C-suite role is getting traction, and it's expected that 90% of large global organizations will have a Chief Data Officer (CDO) on their teams in the coming decade.
According to Pew Research, 91% of Americans "agree" or "strongly agree" that people have lost control over how personal information is collected and used.
Even in this untrusting consumer environment, by 2025, it's estimated that 463 exabytes of data will be created each day globally – that's the equivalent of 212,765,957 DVDs per day.
Though the CDO role is still new, untested, and amorphous, the position is growing more prevalent and more prominent, as digital transformation reshapes global commerce.
A CDO will be responsible for organization-wide governance, management, and exploitation of information.
This data management and exploitation will be executed to achieve superior business intelligence performance, advanced analytics, data mining, machine learning, and artificial intelligence.
The age of the platform nation
Since the First Industrial Revolution, growth and welfare have depended upon increasing the efficiency of production.
The First Industrial Revolution began in England in the late 18th century, following James Watt and his steam engine. (A Second Industrial Revolution would occur late in the 19th century and involve developing the steel industry and giant corporations.)
The launch of corporations, specialization, manufacturing, electricity, and the computer all increased productivity, GDP, wages, and national welfare.
Higher wages spur the consumption of more goods and services and bigger national budgets through tax collection. A virtuous circle of prosperity was created.
Some citizens gained more than others, creating persistent inter-generational inequality, but economic means were enhanced across all significant population segments in absolute terms.
Many now see this relationship – between productive efficiency and economic growth and wages – breaking down in the Fourth Industrial Revolution (4IR).
See Andrew Yang.
Digital technology, automated software, and artificial intelligence create digital societies; mass services are replacing mass manufacturing as the source of welfare and productivity enhancement, and shared assets are supplanting exclusive asset ownership.
The three-century reign of the manufacturing nation is beginning to close.
Welcome to the age of the platform nations.
While some nations will continue to try to compete at a physical level, we are entering a new phase of globalization, where digital technologies are changing the nature of commerce and prosperity.
Protecting the past for nostalgia and votes may sound policy on the campaign trail, but makes little sense for long-term economic security and success.
-Marc
ARTIFICIAL INTELLIGENCE DATA POINTS
AI, deep learning, and machine learning: An a16z video primer. Click here to watch.
Many facial recognition systems misidentify people of color more often than white people, according to a US government study that is likely to increase the skepticism of technology widely used by law enforcement agencies.
"AI will be as central to the white-collar office environment as robotics has been to the production economy," said Mark Muro, senior fellow and policy director of the Metropolitan Policy Program at the Brookings Institution. "They'll fundamentally change what work is and what humans do. And no one gets a free pass."
White House announces a $1 billion plan to create AI, quantum institutes: The Wall Street Journal reports, the effort aims to keep the US globally competitive in artificial intelligence and quantum technologies, administration officials said.
Artificial intelligence is a key component of Apple's broader healthcare initiatives.
Toshiba will spend around 34 billion yen ($321 million) on a new research and development complex in Japan geared toward artificial intelligence and security technologies.
The 2020 CNBC Disruptor 50 companies: Full list - click here.
Top 10:
1. Stripe
2. Coupang
3. Indigo Agriculture
4. Coursera
5. Klarna
6. Tempus
7. Zipline
8. SoFi
9. Neteera
10. Gojek
+ Many of these companies have an existing focus on health care, financial services, cloud connectivity, digital marketing, and online distribution to consumers, and they have seen demand for their core products and services more than double during the economic shutdown.
+ Machine learning and artificial intelligence underlie much of the explosive growth.
TWEET
Was surprised by the # of AI companies Apple has acquired
Would have assumed Google, Amazon of Facebook would be more acquisitive of AI companies
https://interactives.cbinsights.com/artificial-intelligence-acquisitions-by-famga/
#ArtificialIntelligence
@asanwal
Anand Sanwal is the co-founder and CEO of CB Insights. Anand’s previous job was managing a 50 million dollar Innovation Fund for American Express. He also worked at one of the most well-known bubble startups, Kozmo.com, which received the largest amount of funding in NYC history for a tech startup.
Have a great weekend. See you next week.
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Curation and commentary by Marc A. Ross
