Would a self-driving cargo ship have gotten stuck in the Suez Canal?
At Hold The Code, we're fascinated by the way artificial intelligence is being used to solve some of our most complicated problems. In this newsletter, we explore AI applications within the realms of health, finance, and society at large. We hope you enjoy this edition — and we'll be back with more next week. Without further ado...
Therapists or Ther-AI-pists?
There has always been a shortage of mental health professionals (and since the COVID-19 pandemic, the need for their support is more than ever), but could this need be satisfied by AI technology?
Could AI replace your therapist?
Recently, websites, social media platforms, and smartphone applications have been increasingly dispensing mental health advice, often leveraging AI to identify individuals at a high risk of mental illness, define mental illnesses, or ensure the quality of care for those being treated for a mental illness. These algorithms work by consuming large amounts of data gathered from social media, smartphones, electronic health records, and other sources to identify patterns that may indicate that someone may need additional mental health support.
Despite this, these algorithms have caused some major concerns, especially regarding accuracy, biases, or bad advice. Additionally, there are major privacy concerns surrounding private information being shared with unintended parties.
The Wall Street Journal interviewed Dr. John Torous, Dr. Adam Miner, and Dr. Zac Imel about their thoughts on AI and mental health treatment.
Read what they had to say here.
A Bit Eco-Unfriendly
Blockchain technology has had a major boom in the past few years, with cryptocurrencies (such as Bitcoin and Ethereum) seeing massive increases in both economic value and general popularity. However, these technologies are insanely computationally expensive, and their impact on the environment could be quite detrimental.
A bit of background
If you’re feeling a bit lost, here are a few key terms to know:
Blockchain: A decentralized, digital system of recording transactions where each transaction is duplicated and distributed to the entire network of users (Imagine if every user had a ledger that automatically updates for every new transaction in this group of users. This means that no single entity controls the ledger and can take advantage of this power, an issue that plagued digital currencies before this technology was developed.)
Cryptocurrency: a digital currency where transactions are facilitated and recorded by a decentralized system (i.e. blockchain)
Bitcoin: a cryptocurrency (probably the most well-known cryptocurrency)
Ethereum: another cryptocurrency
Non-Fungible Tokens (NFTs): think of these like digital trading cards that are a part of the Ethereum blockchain (other blockchains have these too, but Ethereum is most notable now)
A bit from the experts
Tech magnates Jack Dorsey and Elon Musk have been huge proponents of cryptocurrencies, with Dorsey recently selling an NFT of his first-ever tweet for $2.5 million. However, Bill Gates seems to have some reservations surrounding the environmental impact of Bitcoin and similar technologies, saying “Bitcoin uses more electricity per transaction than any other method known to mankind, and so it’s not a great climate thing.”
A bit of controversy
Estimates of Bitcoin’s carbon footprint vary, but all of them are shockingly high. Alex DeVries, creator of the “Bitcoin Energy Consumption Index,” has equated its energy to that of the entire nation of Chile. Other studies have argued that the environmental impact of cryptocurrencies is overstated. It is also worth noting that there are ways to reduce the consumption of blockchains (like powering computing centers with renewable or more eco-friendly energy sources). Ultimately, whatever estimate you use to tabulate the environmental impact of cryptocurrencies, the cost is still extremely high and constantly growing.
VC Firms Have Long Backed AI. Now, They Are Using It.
AI’s ability to recognize patterns in data and predict outcomes have raised hopes that it can play an integral role in decision-making. Similar bets are now being made on the technology’s applicability to the venture capital field.
How AI can help make investment decisions
According to Garter analyst, Alastair Woolcock, AI models, and simulations will:
Change how financials are reviewed
Shape how teams are assessed
Alter how growth strategies are perceived
Correlation Ventures, a San-Francisco-based firm, is already employing AI. They currently use a machine-learning tool that reviews information extracted from pitch decks and other information submitted by startups. The information is fed into an algorithm trained on data from more than 100,000 venture financing rounds; the algorithm identifies how factors such as team experience or board composition correlate with future investor returns. The system then generates a score for the subject, which is intended to speed up the investment process.
The predictive capabilities of AI, coupled with the technology’s ability to parse large amounts of data, have made many firms optimistic about the future prospects of using AI. In fact, according to a recent Garter Inc. forecast, “AI will be involved in 75% of venture capital decisions by 2025, up from less than 5% today.
Read the full article here.
Weekly Feature: AI Should Augment Human Intelligence, Not Replace It
In a recently published piece in the Harvard Business Review, David De Cremer and Garry Kasparov challenge the commonly held belief that smart machines will truly replace human workers. Though they don’t deny that in some industries—like manufacturing, service-delivery, and recruitment—intelligent systems have altered labor markets and changed hiring needs, they ultimately contend humans and machines are not in competition with each other. In fact, Cremer and Kasparov envision a future where human intelligence is complemented by artificial intelligence, not outsourced by it.
Machine intelligence vs. human intelligence
According to Cremer and Kasparov, smart machines and humans have different intellectual strengths. They write that “AI-based machines are fast, more accurate, and consistently rational, but they aren’t intuitive, emotional or culturally sensitive. And, “it’s exactly these abilities that humans possess which make us effective.”
AI is perfectly suited to work in lower-level routine tasks that are repetitive and take place within a “closed management system” --- they don’t interact with external forces. For example, Amazon began using algorithms to supervise human workers on assembly lines, given the repetitive and highly structured nature of the work. Designed to optimize productivity, the AI is more effective at monitoring workers than human supervisors.
Human abilities, however, are more expensive. Contrary to artificial intelligence, which can only respond to the data available, humans have the ability to imagine, anticipate, feel, and judge changing situations. This type of intelligence is needed when “open management systems” are in place: when members of a team are interacting with the external environment and therefore have to deal with influences from outside.
The third type of intelligence: augmented
When machine intelligence works in tandem with human intelligence, Cremer and Kasparov argue that the third type of intelligence is born: augmented intelligence.
The enhancing, collaborative potential Cremer and Kasparov envision stands in stark contrast to the zero-sum predictions of what AI will do to our society and organizations. They believe that “greater productivity and the automation of cognitively routine work is a boon, not a threat.”
Read the full article here.
Written by Lex Verb and Molly Pribble