This week’s issue of The Loop delves deeply into the recurrent argument regarding the concerns over emerging technologies and their impact on society. It’s fascinating to note that this debate is not a new phenomenon. In the modern era, it has been a part of our societal discourse since the 1950s, as highlighted by Isaac Asimov in his writings. The introduction of personal computers in the 1980s sparked similar discussions, and today, we revisit these concerns with the emergence of artificial intelligence.


The Highlighter

Whenever man develops a new and powerful technology, he inevitably worries about the potential negative consequences.

Isaac Asimov, The Naked Sun (1956)

The Naked Sun is the second instalment in Isaac Asimov’s Robot series, offering a fascinating exploration of Solarian society. This unique world is characterized by its distinct customs, traditions, and culture. It’s a planet where a strictly regulated population coexists with a vast number of robots, outnumbering humans from ten thousand to one.

Asimov’s narrative serves as a platform to delve into the societal implications of technological progress, sparking debates about ethics and the potential adverse effects on society and individuals. Interestingly, these discussions are not confined to the past, as similar considerations about artificial intelligence continue to be relevant today.


Retrofuturism

The Osborne 1, launched in 1981 by Osborne Computer Corporation, is considered the first portable computer in history. It was contained in a briefcase weighing about 11 kg and it cost $ 1,795 (approximately $ 6,112 in today’s dollars).

Osborne 1

The computer featured a 5-inch CRT screen, a keyboard folded to protect the device during transport, and a software package that included the CP/M operating system, a word processor, a spreadsheet, and database programs. Despite its limited portability and small screen, the Osborne 1 paved the way for future developments in portable computers.

In total, approximately 125,000 units of the Osborne 1 were sold. After initial success, the company encountered financial challenges, ultimately resulting in bankruptcy in 1983, just two years after the computer’s launch.

In the 1980s, the success of early personal computers marked a technological revolution, making computing accessible to an increasingly broad audience. Models like the Apple II, Commodore 64, and Osborne 1 captured people’s imaginations, transforming how they worked, played, and communicated.

However, this rapid innovation also sparked significant concerns and resistance. Many were deeply worried about the novelty and complexity of computers, fearing they could replace human jobs, reduce privacy, and create a dangerous dependency on technology. On November 30, 1981, the Ottawa Citizen reported on page 16:

Teachers must fight computers — If teachers don’t stand up to the growing invasion of computers in the classroom, there’s a good chance literacy will disappear in 10 years, says a visiting professor at Charleston University. Microprocessors in the classroom are threatening to replace teachers and the only ones who can halt this are the teachers themselves.

Asimov’s concerns from almost thirty years earlier were the same as those of that time and, regarding artificial intelligence, are the same as today.


Private Investments in AI in 2023

In the analysis of private investments in artificial intelligence globally, a diversified picture emerges between the United States, Europe, and the rest of the world. The data show a marked predominance of the United States, with massive investments amounting to $ 67.22 billion, affirming their undisputed leadership role in the AI sector.

This is reported in the 2024 Stanford University Artificial Intelligence Index Report.

Private Investment in AI 2023

  • While Europe does not match the USA in terms of investment scale, it demonstrates an expanding AI landscape. Germany, Sweden, and France are the main contributors, with investments of 3.78 billion, 1.91 billion, 1.89 billion, and 1.69 billion, respectively. This significant overall investment underscores Europe’s growing environment in the AI field, albeit with a smaller scale of funding compared to the United States.

  • China has emerged as a significant player in the AI sector outside the US and Europe, with investments totalling 7.76 billion. This amount highlights China’s current position as a primary competitor to the USA regarding technological development in AI.

  • Moreover, countries such as India, Japan, and South Korea are exhibiting substantial growth in the sector, with investments ranging from 0.68 to 1.39 billion. These developments point towards a growing interest and technological advancement in the Asian regions.

Based on the data, it appears that the majority of investments in artificial intelligence are concentrated in the United States, with several countries in Europe and Asia also emerging as key players in this field. These nations are becoming increasingly important centers for innovation and development in AI.


$1,8 billion

The global AI market is rapidly growing and is expected to reach $1,8 billion by 2030. This represents significant growth compared to the estimated $196 billion in 2023. The compound annual growth rate is 36.6% from 2024 to 2030. — Grand View Search

97 million jobs

By 2025, it is expected that AI will create 97 million new jobs while eliminating approximately 85 million jobs. This net change of 12 million jobs highlights the need for reskilling and training to adapt to emerging roles as AI transforms numerous sectors. — Market Data Forecast


From Algebrica

Cosine similarity is a technique that can measure the proximity between two documents by transforming words into vectors within a vector space. This transformative approach allows for the semantic interpretation of human language in a format that machines can easily understand.

The cosine similarity between two vectors is calculated using the following formula:

\[ \text{cosine similarity} \ (V_x, V_y) = \frac{\sum_{i=1}^{n} V_{x_i} \cdot V_{y_i}}{\sqrt{\sum_{i=1}^{n} (V_{x_i})^2} \times \sqrt{\sum_{i=1}^{n} (V_{y_i})^2}} \]

or in compact form:

\[\text{cosine similarity} \ (V_x, V_y) = \frac{V_x \cdot V_y}{||V_x|| \ ||V_y||}\]

where:

  • \( V_x \cdot V_y \) is the dot product of the vectors \( A \) and \( B \).
  • \( ||V_x|| \) and \( ||V_y|| \) are the norms (lengths) of the vectors \( V_x \) and \( V_y \).

Worth to Read

  • To make cars safer, connect them to everything, IEEE Spectrum — The emerging area of vehicle-to-everything (V2X) connectivity involves cars communicating wirelessly with each other and with traffic signs and other infrastructures. A paper published in the IEEE Open Journal of Vehicular Technology suggests that V2X, supported by 5G and 6G networks, has the potential to “pave the way for safe, affordable, accessible, and sustainable transport systems, as well as enhance road safety.”

  • The most important AI trends in 2024, IBM Blog — This article explores the key AI trends of 2024, with a focus on enterprise challenges and advancements in model optimization and deployment.

  • OpenAI and Google are launching supercharged AI assistants, MIT Technology Review — Google and OpenAI revealed that they have developed advanced AI assistants. These tools can engage in real-time conversations, adapt when interrupted, analyze live video feeds, and translate conversations instantly.

  • Conformal Language Modeling, Google Research — Conformal prediction is a widely used technique for generating prediction sets from machine learning models with statistically solid performance guarantees.