The controversy fed into much-needed debates on the boundary between poetry and song, but the question of what constitutes literature is much broader. Does it mean the same as it did in 1901 when the first Nobel Prize for Literature was awarded?

High and low culture

These questions date back far beyond 2016. In the late 1950s, a group of professors from the University of Birmingham founded a new interdisciplinary area of study, called cultural studies, in order to ask new questions: What was the role of TV and other mass media in cultural development? Is there a justification for distinguishing high and low culture? What is the relationship between culture and power?

These questions are all still relevant to current debates around literature. Often, the word “literary” is a status symbol, a seal of approval to distinguish “high” culture from more vulgar or less valuable “low” forms of culture. Comics, for example, were not invited to join the club until recently, thanks in part to a rebranding under the more respectable guise of “graphic novels”.

According to the Merriam-Webster dictionary, literature displays “excellence of form or expression and expressing ideas of permanent or universal interest”. It seems that an artist like Bob Dylan can take home the Nobel prize thanks to literature’s defining feature of “excellence of form or expression”, which is not strictly limited to the written word.

But how do we account for other language-based forms of expression? If performed works such as theatre or songwriting can be considered literature, where is the limit?

Word play: text-based video games

According to data from video game data consultancy Newzoo, more than 3 billion people play video games worldwide – almost half of the world’s population. In Spain alone, 77% of young people play videogames, making them a massively relevant form of culture. But what does this have to do with “excellence of form or expression”? To answer this question we have to look back several decades.

When the first video games were developed in the 1950s, two distinct genres emerged: one was action oriented (such as the pioneering 1958 game Tennis for Two), and the other more text based. The original written games, known as “interactive fiction”, were made up exclusively of text, and the player’s job was to read and make decisions that would determine the game’s outcome using a keyboard.

The inclusion of images in adventure games would not arrive until 1980, when Mystery House became the first “graphic adventure” game. These would reach their heyday in the 1990s: famous examples include the first two Monkey Island games (1990, 1991), Day of the Tentacle (1993), Full Throttle (1995), and Grim Fandango (1998), though there were many others. Despite technological advances, these games inherited several features from interactive fiction, including the predominant role of text.

The experience of playing one of these titles is not so different from that of a book: reading, pauses, the possibility of backtracking, and so on. The player spends most of their time in dialogue with various characters in search of information, stories, or even banter and jokes that are irrelevant to the game’s progress, much like footnotes or subplots.

Several classic adventure games even have direct links to literature: The Abbey of Crime (1987) is a Spanish adaptation of Umberto Eco’s The Name of the Rose, while the legendary insult sword fighting of The Secret of Monkey Island was written by science fiction author Orson Scott Card. In Myst (1993), the gameplay itself revolves around two books.

Literature on the screen: “story-rich” games

In more recent years, a new sub-genre of adventure games – known as “story-rich” games – has become popular thanks to independent creators and producers. In Papers, Please (2013), a border policeman in a fictional dictatorial regime deals with terrible moral dilemmas on a daily basis. In Firewatch (2016), players take the role of a forest ranger who investigates a conspiracy by walkie-talkie. In Return of the Obra Dinn (2018), the player must reconstruct a tragedy on the high seas with the help of an incomplete book and a peculiar compass. In all these cases, gameplay and visuals take a back seat to strong narratives.

A quintessential example is The Stanley Parable (2011), where the player takes the role of a worker in a strangely deserted office. They have to explore several corridors while trying unsuccessfully to interact with their surroundings, accompanied by the voice of an enigmatic narrator. Upon reaching a room with two open doors, the voiceover states that Stanley “entered the door on his left”.

The player can choose to follow the instructions or disobey, provoking the wrath of the narrator much like in the denouement of Miguel de Unamuno’s 1914 novel Fog, where the main character speaks directly to the author.

Read More: PlayStation unveils Community Game Help, crowdsources user gameplay

Each decision then opens up new paths leading to dozens of possible endings, similar to a “choose your own adventure” book. Its fragmentary and disordered story – as well as its playful spirit – is reminiscent of Julio Cortázar’s 1963 novel Hopscotch. The experience of playing the game is marked by postmodern literary features – as described by critics like Mikhail Bakhtin or Linda Hutcheon – including metafiction, intertextuality and parody.

One of its creators – Davey Wreden, a critical studies graduate – also created The Beginner’s Guide (2015), a game in which the player moves through levels of failed video games to learn more about their mysterious creator. In one, the player’s task consists solely of wandering through a virtual cave reading the countless comments left there by other frustrated players.

In recent years, the genre of digital or electronic literature has emerged, including books with QR codes, works that can only be read with virtual reality headsets, poetry collections published as apps, and so on. These works are fundamentally based on language, begging the question of why video games cannot also fit into this category.

This debate takes on added relevance today, as digital formats are having an undeniable impact on our reading habits. Just as today we accept oral cultures or popular music as literature, perhaps one day we will do the same with interactive stories like The Stanley Parable. Writing has always tried to break away from established ideas, and we know that literature is not limited to words on paper. Sometimes it pays to disobey the voice in our heads and walk through the door on the right, the one that leads to new, unexplored possibilities.

• Andrés Porras Chaves is a Professor of Humanities, IE University
• This article first appeared in The Conversation

A definition of empathy

As a psychologically informed philosopher, I define genuine empathy according to three criteria:

• Congruence of feelings: empathy requires that the person who empathizes to feel what it is like to experience the other’s emotions in a specific situation. This distinguishes empathy from a mere rational understanding of emotions.
• Asymmetry: the person who feels empathy only has the emotion because another individual has it and it is more appropriate to the other’s situation than to their own. For this reason, empathy is not just a shared emotion like the shared joy of parents over the progress of their offspring, where the asymmetry condition is not met.
• Other-awareness: There must be at least a rudimentary awareness that empathy is about the feelings of another individual. This accounts for the difference between empathy and emotional contagion which occurs if one catches a feeling or an emotion like a cold. This happens, for instance, when kids start to cry when they see another kid crying.

Empathetic AI or psychopathic AI?

Given this definition, it’s clear that artificial systems cannot feel empathy. They do not know what it’s like to feel something. This means that they cannot fulfil the congruence condition. Consequently, the question of whether what they feel corresponds to the asymmetry and other-awareness condition does not even arise. What artificial systems can do is recognise emotions, be it on the basis of facial expressions, vocal cues, physiological patterns or affective meanings; and they can simulate empathic behaviour by ways of speech or other modes of emotional expression.

Artificial systems hence show similarities to what common sense calls a psychopath: despite being unable to feel empathy, they are capable to recognize emotions on the basis of objective signs, to mimic empathy and to use this ability for manipulative purposes. Unlike psychopaths, artificial systems do not set these purposes by themselves, but are given them by their designers. So-called empathetic AI is often supposed to make us behave in a desired way, such as not getting upset when driving, learning with greater motivation, working more productively, buying a certain product – or voting for a certain political candidate. But then does not everything depend on how good the purposes are for which empathy-simulating AI is used?

Empathy-simulating AI in the context of care and psychotherapy

Take care and psychotherapy, which aim to nurture people’s well-being. You might think that the use of empathy-simulating AI in these areas is definitely a good thing. Would they not be wonderful care-givers and social companions for old people, loving partners for the disabled, or perfect psychotherapists that have the benefit of being available 24/7?

Such questions ultimately concern what it means to be a human being. Is it enough for a lonely, old or mentally disturbed person to project emotions onto an artefact devoid of feelings, or is it important for a person to experience recognition for themselves and their suffering in an interpersonal relationship?

Respect or tech?

From an ethical perspective, it is a matter of respect whether there is someone who empathically acknowledges the needs and the suffering of a person as such. By taking away recognition by another subject, the person in need of care, companionship or psychotherapy is treated as a mere object because ultimately this is based on the assumption that it does not matter whether anybody really listens to the person. They do not have a moral claim that their feelings, needs and suffering is perceived by someone who can really understand them. Using empathy-simulating AI in care and psychotherapy is ultimately another case of technological solutionism, i.e., the naïve assumption that there is a technological fix for every problem, including loneliness and mental “malfunctions”. Outsourcing these issues to artificial systems prevents us from seeing the social causes for loneliness and mental disorders in the larger context of society.

In addition, designing artificial systems to appear as someone or something that has emotions and feels empathy would mean that such devices always have a manipulative character because they address very subliminal mechanisms of anthropomorphisation. This fact is used in commercial applications to get users to unlock a paid premium level: or customers pay with their data. Both practices are particularly problematic for vulnerable groups, which are at stake here. Even people who do not belong to vulnerable groups and are perfectly aware that an artificial system has no feelings will still react empathically to it as if it did.

Empathy with artificial systems – all too human

It is a well-studied phenomenon that humans react with empathy towards artificial systems that display certain human or animal-like characteristics. This process is largely based on perceptual mechanisms which are not consciously accessible. Perceiving a sign that another individual is undergoing a certain emotion produces a congruent emotion in the observer. Such a sign can be a typical behavioural manifestation of an emotion, a facial expression or an event that typically causes a certain emotion. Evidence from brain MRI scans shows that the same neural structures get activated when humans feel empathy with robots.

Read More: Increasingly sophisticated AI systems can perform empathy, but their use in mental health care raises ethical questions

Although empathy might not be strictly necessary for morality, it plays an important moral role. For this reason, our empathy toward human-like (or animal-like) robots imposes at least indirect moral constraints on how we should treat these machines. It is morally wrong to habitually abuse robots that elicit empathy as doing so negatively affects our capacity to feel empathy, which is an important source of moral judgment, motivation, and development.

Does this mean that we have to establish a robot-rights league? That would be premature, as robots do not have moral claims by themselves. Empathy with robots is only indirectly morally relevant due to its effects on human morality. But we should carefully consider whether and in which areas we really want robots that simulate and evoke empathy in human beings as we run the risk of distorting or even destroying our social practices if they became pervasive.

• Catrin Misselhorn is a Professor of philosophy, Georg-August-Universität Göttingen
• This article first appeared in The Conversation

Similarly, today’s experts warn that an artificial general intelligence (AGI) doomsday is imminent. Others retort that large language models (LLMs) have already reached the peak of their powers.

It’s difficult to argue with David Collingridge’s influential thesis that attempting to predict the risks posed by new technologies is a fool’s errand. Given that our leading scientists and technologists are usually so mistaken about technological evolution, what chance do our policymakers have of effectively regulating the emerging technological risks from artificial intelligence (AI)?

We ought to heed Collingridge’s warning that technology evolves in uncertain ways. However, there is one class of AI risk that is generally knowable in advance. These are risks stemming from misalignment between a company’s economic incentives to profit from its proprietary AI model in a particular way and society’s interests in how the AI model should be monetised and deployed.

The surest way to ignore such misalignment is by focusing exclusively on technical questions about AI model capabilities, divorced from the socio-economic environment in which these models will operate and be designed for profit.Focusing on the economic risks from AI is not simply about preventing “monopoly,” “self-preferencing,” or “Big Tech dominance”. It’s about ensuring that the economic environment facilitating innovation is not incentivising hard-to-predict technological risks as companies “move fast and break things” in a race for profit or market dominance.

It’s also about ensuring that value from AI is widely shared, by preventing premature consolidation. We’ll see more innovation if emerging AI tools are accessible to everyone, such that a dispersed ecosystem of new firms, start-ups, and AI tools can arise.

OpenAI is already becoming a dominant player with US$2 billion (£1.6 billion) in annual sales and millions of users. Its GPT store and developer tools need to return value to those who create it in order to ensure ecosystems of innovation remain viable and dispersed.

By carefully interrogating the system of economic incentives underlying innovations and how technologies are monetised in practice, we can generate a better understanding of the risks, both economic and technological, nurtured by a market’s structure. Market structure is not simply the number of firms, but the cost structure and economic incentives in the market that follow from the institutions, adjacent government regulations, and available financing.

Degrading quality for higher profit

It is instructive to consider how the algorithmic technologies that underpinned the aggregator platforms of old (think Amazon, Google and Facebook among others) initially deployed to benefit users, were eventually reprogrammed to increase profits for the platform.

The problems fostered by social media, search, and recommendation algorithms was never an engineering issue, but one of financial incentives (of profit growth) not aligning with algorithms’ safe, effective, and equitable deployment. As the saying goes: history doesn’t necessarily repeat itself but it does rhyme.

To understand how platforms allocate value to themselves and what we can do about it, we investigated the role of algorithms, and the unique informational set-up of digital markets, in extracting so-called economic rents from users and producers on platforms. In economic theory, rents are “super-normal profits” (profits that are above what would be achievable in a competitive market) and reflect control over some scarce resource.

Importantly, rents are a pure return to ownership or some degree of monopoly power, rather than a return earned from producing something in a competitive market (such as many producers making and selling cars). For digital platforms, extracting digital rents usually entails degrading the quality of information shown to the user, on the basis of them “owning” access to a mass of customers.

For example, Amazon’s millions of users rely on its product search algorithms to show them the best products available for sale, since they are unable to inspect each product individually. These algorithms save everyone time and money: by helping users navigate through thousands of products to find the ones with the highest quality and the lowest price, and by expanding the market reach of suppliers through Amazon’s delivery infrastructure and immense customer network.

These platforms made markets more efficient and delivered enormous value both to users and to product suppliers. But over time, a misalignment between the initial promise of them providing user value and the need to expand profit margins as growth slows has driven bad platform behaviour. Amazon’s advertising business is a case in point.

Amazon’s advertising

In our research on Amazon, we found that users still tend to click on the product results at the top of the page, even when they are no longer the best results but instead paid advertising placements. Amazon abuses the habituated trust that users have come to place in its algorithms and instead allocates user attention and clicks to inferior quality, sponsored, information from which it profits immensely.

We found that, on average, the most clicked sponsored products (advertisements) were 17% more expensive and 33% lower ranked according to Amazon’s own quality, price, and popularity optimising algorithms. And because product suppliers must now pay for the product ranking that they previously earned through product quality and reputation, their profits go down as Amazon’s go up, and prices rise as some of the cost is passed on to customers.

Amazon is one the most striking examples of a company pivoting away from its original “virtuous” mission (“to be the most customer-centric company on Earth”) towards an extractive business model. But it is far from alone.

Google, Meta, and virtually all other major online aggregators have, over time, come to preference their economic interests over their original promise to their users and to their ecosystems of content and product suppliers or application developers. Science fiction writer and activist Cory Doctorow calls this the “enshittification” of Big Tech platforms.

But not all rents are bad. According to the economist Joseph Schumpeter, rents received by a firm from innovating can be beneficial for society. Big Tech’s platforms got ahead through highly innovative, superior, algorithmic breakthroughs. The current market leaders in AI are doing the same.

So while Schumpeterian rents are real and justified, over time, and under external financial pressure, market leaders began to use their algorithmic market power to capture a greater share of the value created by the ecosystem of advertisers, suppliers and users in order to keep profit growing.

User preferences were downgraded in algorithmic importance in favour of more profitable content. For social media platforms, this was addictive content to increase time spent on platform at any cost to user health. Meanwhile, the ultimate suppliers of value to their platform – the content creators, website owners and merchants – have had to hand over more of their returns to the platform owner. In the process, profits and profit margins have become concentrated in a few platforms’ hands, making innovation by outside companies harder.

A platform compelling its ecosystem of firms to pay ever higher fees (in return for nothing of commensurate value on either side of the platform) cannot be justified. It is a red light that the platform has a degree of market power that it is exploiting to extract unearned rents. Amazon’s most recent quarterly disclosures (Q4, 2023), shows year-on-year growth in online sales of 9%, but growth in fees of 20% (third-party seller services) and 27% (advertising sales).

What is important to remember in the context of risk and innovation is that this rent-extracting deployment of algorithmic technologies by Big Tech is not an unknowable risk, as identified by Collingridge. It is a predictable economic risk. The pursuit of profit via the exploitation of scarce resources under one’s control is a story as old as commerce itself.

Technological safeguards on algorithms, as well as more detailed disclosure about how platforms were monetising their algorithms, may have prevented such behaviour from taking place. Algorithms have become market gatekeepers and value allocators, and are now becoming producers and arbiters of knowledge.

Risks posed by the next generation of AI

The limits we place on algorithms and AI models will be instrumental to directing economic activity and human attention towards productive ends. But how much greater are the risks for the next generation of AI systems? They will shape not just what information is shown to us, but how we think and express ourselves. Centralisation of the power of AI in the hands of a few profit-driven entities that are likely to face future economic incentives for bad behaviour is surely a bad idea.

Thankfully, society is not helpless in shaping the economic risks that invariably arise after each new innovation. Risks brought about from the economic environment in which innovation occurs are not immutable. Market structure is shaped by regulators and a platform’s algorithmic institutions (especially its algorithms which make market-like allocations). Together, these factors influence how strong the network effects and economies of scale and scope are in a market, including the rewards to market dominance.

Technological mandates such as interoperability, which refers to the ability of different digital systems to work together seamlessly; or “side-loading”, the practice of installing apps from sources other than a platform’s official store, have shaped the fluidity of user mobility within and between markets, and in turn the ability for any dominant entity to durably exploit its users and ecosystem. The internet protocols helped keep the internet open instead of closed. Open source software enabled it to escape from under the thumb of the PC era’s dominant monopoly. What role might interoperability and open source play in keeping the AI industry a more competitive and inclusive market?

Disclosure is another powerful market-shaping tool. Disclosures can require technology companies to provide transparent information and explanations about their products and monetisation strategies. Mandatory disclosure of ad load and other operating metrics might have helped to prevent Facebook, for example, from exploiting its users’ privacy in order to maximise ad dollars from harvesting each user’s data.

But a lack of data portability, and an inability to independently audit Facebook’s algorithms, meant that Facebook continued to benefit from its surveillance system for longer than it should have. Today, OpenAI and other leading AI model providers refuse to disclose their training data sets, while questions arise about copyright infringement and who should have the right to profit from AI-aided creative works. Disclosures and open technological standards are key steps to try and ensure the benefits from these emerging AI platforms are shared as widely as possible.

Market structure, and its impact on “who gets what and why”, evolves as the technological basis for how firms are allowed to compete in a market evolves. So perhaps it is time to turn our regulatory gaze away from attempting to predict the specific risks that might arise as specific technologies develop. After all, even Einstein couldn’t do that.

Instead, we should try to recalibrate the economic incentives underpinning today’s innovations, away from risky uses of AI technology and towards open, accountable, AI algorithms that support and disperse value equitably. The sooner we acknowledge that technological risks are frequently an outgrowth of misaligned economic incentives, the more quickly we can work to avoid repeating the mistakes of the past.

We are not opposed to Amazon offering advertising services to firms on its third-party marketplace. An appropriate amount of advertising space can indeed help lesser-known businesses or products, with competitive offerings, to gain traction in a fair manner. But when advertising almost entirely displaces top-ranked organic product results, advertising becomes a rent extraction device for the platform.

---

An Amazon spokesperson said:

We disagree with a number of conclusions made in this research, which misrepresents and overstates the limited data it uses. It ignores that sales from independent sellers, which are growing faster than Amazon’s own, contribute to revenue from services, and that many of our advertising services do not appear on the store.

Amazon obsesses over making customers’ lives easier and a big part of that is making sure customers can quickly and conveniently find and discover the products they want in our store. Advertisements have been an integral part of retail for many decades and anytime we include them they are clearly marked as ‘Sponsored’. We provide a mix of organic and sponsored search results based on factors including relevance, popularity with customers, availability, price, and speed of delivery, along with helpful search filters to refine their results. We have also invested billions in the tools and services for sellers to help them grow and additional services such as advertising and logistics are entirely optional.

---

• Tim O’Reilly is a Visiting Professor of Practice at the UCL Institute for Innovation and Public Purpose, UCL
• Ilan Strauss is a Head of Digital Economy Research, UCL
• Mariana Mazzucato is a Professor in the Economics of Innovation and Public Value and Founding Director of the UCL IIPP, UCL
• Rufus Rock is a Researcher, Institute for Innovation and Public Purpose, UCL
• This article first appeared in The Conversation

But two companies recently came unstuck when the United States Securities and Exchange Commission (SEC) accused them of exaggerating their use of AI, marking the world’s first significant move in combating so-called “AI washing”.

Delphia (USA) Inc. and Global Predictions Inc., boasted about using AI for designing investment strategies, but the SEC found their claims to be unsubstantiated.

There’s much speculation around AI, especially with the generative technology app ChatGPT shaking things up. But amid all the hype, AI washing is becoming more common.

As well as exaggerating or misrepresenting their AI capabilities, companies may inflate the abilities of AI algorithms or create the illusion AI plays a more significant role than it actually does.

What’s so good about AI?

Incorporating AI into business operations has many benefits. It can streamline processes, quickly break down and analyse complex data to speed up decision-making and help organisations stay ahead in a rapidly evolving and competitive market.

Promoting its use of AI helps portray a company as high-tech and cutting-edge, even if the reality doesn’t check out.

The practice of AI washing isn’t totally new. It follows the same idea as greenwashing, where companies pretend to be eco-friendly to attract investors and consumers.

It involves dressing up ordinary tech with fancy AI buzzwords such as “machine learning”, “neural networks”, “deep learning” and “natural language” processing to seem more innovative than they actually are.

AI and the finance sector

AI washing flourishes in finance and investment because of the industry’s high-stakes, intense competition and the seductive appeal of technology-driven solutions.

AI’s algorithms can analyse extensive datasets, enhance predictability and uncover hidden patterns in financial data. And AI’s real-time processing capabilities enable dynamic adaptation to market changes.

The complexity of financial products provides room for firms to conceal the reality behind flashy AI claims. And the lack of regulation exacerbates the problem.

Despite AI’s impressive capabilities, it’s not without its drawbacks, including ethical concerns, susceptibility to cyber-attacks and manipulation, and the lack of transparency in how AI algorithms arrive at decisions or predictions.

Read More: Generative AI – What it is and how it can affect your business

Supporters of AI-related investments range from novice retail investors to seasoned institutional players.

Such interest has led to venture capital firms allocating more capital to AI startups last year than they did previously.

A lack of regulation

But without clear guidelines, firms can exploit loopholes and mislead investors.

This lack of oversight erodes trust and credibility in the industry. AI washing may also stifle innovation. If investors are skeptical about AI, they’re less likely to invest in legitimate AI-powered solutions. This can slow down the development of truly groundbreaking technologies.

It is crucial to deal with AI washing, echoing the cautionary tale of the dot-com bubble. Much like the exaggerated promises and speculative fervor surrounding internet companies which led to market turbulence and investor scepticism in the late 1990s, the hype surrounding AI capabilities in finance poses similar risks.

AI washing could lead investors to pour money into AI-related ventures without fully understanding the risks or potential limitations, ultimately exposing them to financial losses when the bubble bursts.

The European Union AI Act is the first regulation in the world to govern the use, development, disclosure and oversight of AI. But in Australia there are no specific laws. Regulation currently falls under the Corporations Act.

ASIC is currently considering ways to regulate AI, including formulating penalties for AI washing.

Holding companies accountable for accurate information about technology applications helps maintain the integrity of financial markets and ensures fairness for investors.

How to spot AI washing

So, how can you, as an investor or consumer, avoid falling victim to AI washing? Here are some tips:

1. Verify registration status and credentials

Before buying or investing in anything claiming AI capabilities, verify the investment company’s registration status and credentials by looking them up on the professional register. Ensure they have no disciplinary [history] by checking the Australian Securities and Investment Commission register.

2. Be cautious with AI-focused investments

Investing in AI-driven companies may seem promising, but be wary of companies that tout their “revolutionary” or “industry-leading” AI without providing specifics. What exactly makes their AI revolutionary? What problems does it solve? Companies that rely on empty buzzwords without concrete details are probably exaggerating their capabilities.

3. Boost your knowledge

Get a grasp of AI and machine learning basics. Learn common AI techniques and terms used in finance. There are a large number of free resources online for beginners.

4. Ask questions

Do not solely rely on AI-generated information for investment decisions. AI-generated data may be based on inaccurate or biased. Ask financial advisors and companies about their specific AI implementation. What kind of data are they using? How are their algorithms trained? What are the limitations of their technology?

5. Be sceptical of high returns with little to no risk

Be sceptical of financial products promising high returns with minimal risk, especially those claiming AI-powered success. This tactic is a common red flag for AI washing. Don’t rely solely on a company’s claims – conduct independent research by following the financial news or checking regulatory filings of companies before investing.

• Angel Zhong is an Associate Professor of Finance, RMIT University
• This article first appeared in The Conversation

Sagrada Familia | Carrer de Mallorca, 401, 08013 Barcelona

Gaudi’s Sagrada Familia cathedral is a wonder to behold, not least because it’s still incomplete over a hundred years later. It took building technology that long to catch up to Gaudi’s vision. But the invention of concrete reinforced with steel has enabled the cathedral’s amazing and detailed tall spires to finally be completed. I am mesmerised every time I go there. The sheer size of the building, which was started in 1882 although Gaudi only took over as architect the following year, is as mind-blowing as the attention to details – and there are many, many details.

Inspired by nature, Gaudi wove these influences into all his work. The giant five-story columns look like the branches of small sapling trees, while the spectacular starburst ceiling feel like sunflowers looking down at you. You can spend half an hour outside the main doors just standing and staring at the incredible embellishment of the building. The themes are obviously biblical and are abundant. It’s well worth the visit as long as you book well in advance.

I’m lucky to have been invited to the annual MWC Barcelona telecoms conferences quite regularly over the last 15 years and have seen the progress. Watch this video to see what it will ultimately look like when it’s finished. Time Magazine also has a great feature on it too. You have to book online at sagradafamilia.org.

Casa Batlló | Passeig de Gràcia, 43, 08007 Barcelona

Casa Batlló is another Antoni Gaudi’s pieces, and is his most mature work to date, not least an architectural and creative feat. It’s my favourite of his buildings and the rooftop arches are arguably the most beautiful. The rooves of Barcelona buildings were used as the laundry and were dank, smelly places. But his famed Catalonian arches are pure genius, allowing in light and wind but keeping out rain.

Get the audio guide when you visit, because it reveals useful info about the various details. You have to book online at casabatllo.es.

Casa Mila | Provença, 261-265, 08008 Barcelona

Casa Batlló is situated on Passeig de Gràcia, essentially Barcelona’s Fifth Ave, an upper-class street with all the major fashion houses. A few blocks up the road is Casa Mila, nicknamed La Pedrera (the quarry), which was Gaudi’s attempt at a block of flats and communal living.

It’s perhaps architecturally less impressive, but still very interesting and worth the tour. The roof is awesome, with elegant chimneys that are artworks more than exhausts. The views from the roof are exquisite, of surrounding Barcelona, including Sagrada Familia.

Passeig de Gràcia | Barcelona

Passeig de Gràcia is a great place to shop all the luxury and high street brands. It continues up the drag, as it were, from las Ramblas and the grand Plaça de Catalunya square.

Think of it as a high-class shopping centre, in the old-fashioned European style. It’s a classic high street, that just happens to have two of the most amazing Gaudi buildings on it. It’s also a great place to grab little tapas.

Muji Passeig de Gràcia | Passeig de Gràcia, 21, 08007 Barcelona

The good news is Muji has opened several stores, including a flagship store on this main drag. I am a huge fan of Muji, which is Japanese for “brand of no brand”. Its simple elegance is quintessential Japanese design, with practicality as important as aesthetics. Muji’s travel accessories are legendary. The 50ml bottles are the ideal size for flying, as are the thoughtful pouches and other must-haves. Half of my own essential travel gear is from Muji. This is an ode I once wrote to its minimalist elegance.

Park Güell | Gràcia, 08024 Barcelona

Another great Gaudi creation is Park Güell. Situated on a hill, it has panoramic views of Barcelona and the famous dragon stairway. You’ll recognise it by the hoards taking selfies with the dragon itself. Inside the park are two of his buildings that have some of his furniture and other things from that period.

Barcelona Pavilion | Av. de Francesc Ferrer i Guàrdia, 7, Sants-Montjuïc, 08038

Near the original Fira conference venue, where MWC Barcelona used to be held, are a few interesting nearby attractions. A short walk up the hill is arguably the most important piece of modern-day architecture: Ludwig Mies van der Rohe’s iconic Barcelona Pavilion.

You’ll instantly recognise the floor-to-ceiling glass walls that predominate now from his eternal design, and the Barcelona chairs he made to place inside it.

Considering it was built in 1929 as the German Pavilion for the World Exposition (this is a reconstruction rebuilt in the 1980s), it’s a sign of how profoundly good his design genius was. The chairs, incidentally, were a gift of the king and queen of Spain at the time. I think of this glorious house as the birthplace of modern architecture.

Parc de Joan Miró | C/ d’Aragó, 2, L’Eixample, 08015

Another famed son of Barcelona is Joan Miró, who designed the logo of La Caixo bank – surely the only bank to be able to boast such a distinguished designer. Just near the Fira is Miró’s famed 22m high Woman and Bird sculpture, which is worth a look. Head away from the Fira, across Plaza Espanya, passed the revamped bull ring (which is now a shopping centre, talk about the centre of popular culture moving from bullfighting to shopping) to see it.

La Rambla | 08002 Barcelona

La Rambla is the famous street that tourists flock to, and the best place to experience Barcelona’s notoriously good pickpockets. Be especially aware of them near the Fira too. Walk down La Rambla for the experience, but beware: it’s tourist trap hell. Do it once, if you decide to go down to the waterfront, which is a great place for a meal too.

Eating out in Barcelona

My favourite dish is jamón ibérico, the acorn-fed, cured ham leg that is marbled with juicy fat and tastes like Spain. Catalan cuisine is slightly different from Spanish, and the local bread is often smeared with a tomato/olive oil/garlic spread that is also very traditional. It’s often served as a starter in restaurants, but I’ve often had it for lunch. It’s called “pan con tomate” (in Spanish) or “pan tumaca” (in Catalan). At very function, there will be a cured leg that a waiter slowly, and painstakingly, cuts off slivers of this tantalisingly good pork.

Smallish baguettes are also on sale for a few euros, including at the Fira conference venue (buy one at 10am for lunch, as the queues at lunchtime are prohibitively long) and the airport. It’s usually my last meal.

Try all the other tapas dishes, as per your tastes, but the paella is a must. It’s considered a lunch meal, not strictly for dinner, but it’s always available.

You can pretty much get a good meal at any bistro in Barcelona. These are a few I have eaten at:

Mi Burrito y Yo | Carrer del Pas de l’Ensenyança, 2, Ciutat Vella, 08002

If you want a meal in the old Gothic Quarter (and you do) then this quirky restaurant is a good one. I walked passed this restaurant last year with a bunch of other people and randomly chose to sit down and have a meal. Needless to say, the experience was wonderful and the food excellent.

Mercat de Santa Caterina | Av. de Francesc Cambó, 16, 08003

For breakfast, or just for some fresh fruit, go to arguably the most beautiful food market you’ll ever see. Mercat de Santa Caterina was the old market that has since been given a glorious upgrade.

I’ve also eaten at Ciutat Comtal, a very traditional Catalan bistro which serves great tapas. Quimet & Quimet is yet another legendary restaurant that’s worth the visit.

Canal+, unlike its forebears, isn’t backing down. It’s desperately looking for an ‘in’ to Africa’s content scheme, and it’s hoping to do that through a total buyout of MultiChoice, owners of DStv and SuperSport. Despite turning down the French firm’s initial offer in February, Canal+ returned with a vastly improved offer that, by the look of it, MultiChoice is deeply considering.

According to MyBroadband, MultiChoice has informed shareholders that an independent board has been established to seriously consider Groupe Canal+’s offer to purchase the remainder of shares it does not already own, valuing the broadcaster at roughly R55 billion. The deal, a cash buyout, would be worth around R35 billion.

MultiChoice in a statement issued on Monday noted that it had selected Standard Bank as the deal’s independent expert, allowing the bank to review the terms, provide an opinion on Canal+’s offer, and recommend a course of action to its shareholders.

Separate from the deal, Canal+ has continued to dig itself deeper into the company, MultiChoice confirmed on Friday, 5 April. Canal+ now owns 36.6% of the company, up from the 35.01% it held when it upped its bid to R125/share in March, though should the share price rise to above R125/share before the deal closes, Canal+ may be forced to up its offer.

Both MultiChoice and Canal+ intend to post a combined circular to MultiChoice shareholders by 7 May 2024, according to TechCentral.

Source

Spotify’s fiddling with AI again

It was only a matter of time before Spotify got ’round to throwing generative AI onto their platform. It’s been toying with the idea of artificial intelligence for ages, first doling out X, the AI DJ, to users before saddling them with a whole lot of other AI-powered features, Now, however, the platform will allow users to hit a couple of keys on their keyboard (typing) and have an entire playlist spat out back at them. Say hello to AI playlists.

Spotify announced the feature yesterday, although it’s reserved for customers in the UK and Australia for now. When it eventually reaches South Africa, users can type in prompts such as “a playlist that makes me feel like the main character,” or “relaxing music to tide me over during allergy season.”

Get creative with those prompts. Spotify reckons the intelligent platform will understand references to places, animals, activities, movie characters, colours and “even emojis”. Describe your moods and the decades of music you’re after to get the best results. As for when the feature will see a wider rollout, there’s no way of telling. We’re hoping it’ll be sooner rather than later.

Eskom is dropping R67 billion to pollute less

When South Africa’s state-owned power utility, Eskom, isn’t load shedding the country, it’s unfortunately burning more coal than your uncle on a Sunday afternoon. It’s now looking to fix that, according to Bloomberg, by spending billions of rands to cut emissions down to a healthier figure.

Specifically, Eskom is committing to a plan that will see the utility spend R67 billion from now until 2035 to cut its most dangerous emissions by 70% in that timeframe.

Why now? Last week, the Centre for Research on Energy and Clean Air said that the government’s idea of delaying the closure of certain plants could result in the death of tens of thousands of residents.

“Eskom has developed station-specific recovery plans for each station and these include emission-improvement plans which are being implemented and are beginning to show success,” the company said in a response to queries.

Unfortunately, Eskom provided nothing more than ambitious promises and vague sentiments on how it’s planning to cut down on those emissions.

Source

Elon Musk wants a Tesla robotaxi, is getting a robotaxi

According to a new tweet from Elon Musk, the auto-automobile manufacturer is currently working on the long-promised robotaxi, which will be revealed to the world at an event (presumably streamed exclusively on X) on 8 August 2024.

The robotaxi’s unveiling announcement came the same day as a Reuters report noting that Tesla had shelved plans to develop a cheaper car, costing roughly $25,000, with company head Elon Musk telling employees that he’d rather focus on an autonomous vehicle that would make other vehicles “obsolete.”

The idea of a Tesla robotaxi isn’t entirely new. Musk first teased the vehicle at a 2023 event while unveiling the company’s third ‘Master Plan’, though the car was under a cover and left to the imagination. Even earlier, Musk had brought up ideas of a vehicle that would allow customers to earn money from their car by sending it out to complete passenger pick-ups and drop-offs.

Whether the idea of a Tesla robotaxi will all play out how Musk imagines remains to be seen. It will require considerable faith from customers and passengers, a difficult feat after the bad press the company’s FSD (full self-driving) feature has received in the past few years.

Source

Sure, ‘IBM PC‘ might evoke people doing dull things in dull offices, rather than the cool things people do with computers today. But this was 1981. IBM‘s debut PC, the 5150, revolutionised the market … despite the fact that it failed to outperform its rivals and was light on features unless you became light of wallet buying a load of upgrades prior to plugging the thing in. But to borrow a later Apple phrase, IBM quickly won out because it could ‘think different’.

Hang on, IBM was a lumbering monolith. How did it think different?

By doubling down on the notion of not being unique. That sounds counter-intuitive, but bear in mind the market at the time was all about proprietary everything. People were tired of that, so IBM upended the industry by basing its system on standard off-the-shelf parts and open architecture. Users were encouraged to poke around. Third parties could plug holes in the ecosystem and then expand on it – all without permission or interference from  IBM. The 5150 was an instant success and IBM quickly dominated.

So why aren’t we all using IBM PCs, IBM phones and IBM smartwatches today?

That ‘open’ thing was a double-edged sword. Inevitably, the industry bellowed: “Send in the clones!’ Within a year, IBM was staring at cheap ‘copies’ of its creation. Before long, no one was referring to ‘IBM PCs’ nor even ‘IBM-compatible PCs’. Compaq, Microsoft and Intel called the shots. And by the mid-2000s, IBM had exited the market it created. That might sound like failure, but as the late Charles Pankenier, IBM’s director of comms for the PC, once said, its legacy is nothing less than legitimising computing at the personal level.

The report, via the Financial Times, reckons that the choice is being made in response to the expense of developing and maintaining AI systems within the company. It’s not an unusual call to make. Users keen on the latest features from OpenAI are expected to hand over a monthly premium for the same reason.

Smart, Google

The company feels that its new search features, reportedly a single response to search queries in a similar manner to that offered by ChatGPT and other AIs, are worth charging for. Other features will probably also fall under this banner as they are developed. The main question is how to charge users.

It’s not immediately clear whether Google will create an all-new subscription service for users. It’s more likely that folks who are already giving the company money will gain access to enhanced artificial intelligence assistance for search queries — if that’s what they want. A small subset of users are already testing new features but others will have to sign up for access when it becomes more broadly available.

Read More: Google Gemini replaces Bard as catch-all AI platform

It’s a slick idea and one that might drive adoption of existing paid-for Google products. AI is increasingly being used to tempt users over to a particular brand rather than acting as a standalone product. Microsoft’s Edge browser, Adobe’s services, and many others are touting AI integration. Even Mercedes is bragging about ChatGPT integration. The search giant probably will have little issue putting this idea over with its users.

Source

Many cars that compete in this race look more like amusement park rides or science fiction vehicles than the cars you see on the road. That tells you something about why solar cars aren’t an option for everyday travel, at least not yet.

Collecting enough sunlight

While a lot of sunlight falls on Earth during the day, the light becomes scattered as it travels through the atmosphere, so the amount that hits any given surface is fairly low. Averaged out over a full year to remove the effects of different seasons, it’s about 342 watts per square meter, an area equivalent to about 10 square feet. That’s approximately enough power to run a standard refrigerator.

Car sizes vary a lot, but a full-size car in the U.S. is about 18 feet long and 6 feet wide, so it has about 100 to 110 square feet (9 to 10 square meters) of horizontal surface. That would collect roughly 3,420 watts – enough to run a refrigerator, a dishwasher and a microwave oven.

Large solar farms that send electricity to cities and towns compensate for the fact that sunlight is spread across such a large area by putting up millions of solar panels across thousands of acres. Some, mainly in desert areas, use fields of mirrors to concentrate the Sun’s energy. But a standard car doesn’t have enough surface area to collect a lot of solar energy.

Turning sunlight to energy

Another issue is that today’s solar panels aren’t very efficient at converting sunlight into electricity. Typically, their efficiency is around 20%, which means they convert about one-fifth of the solar energy that reaches them into electric current.

This means that 3,420 watts of solar power falling on an average car covered with solar panels would yield only about 684 watts that the car could use. In comparison, it takes about 20,000 watts for an electric vehicle to drive at 60 miles per hour (100 kilometres per hour).

Vehicles that compete in the World Solar Challenge tend to be large and have designs that maximize their horizontal surface area. This helps them collect as much sunlight as possible. As a concept vehicle, that’s fine, but most models don’t have many windows, or space for anything except a driver.

When the Sun doesn’t shine

Yet another challenge is that geographic locations, daylight hours and weather conditions all affect how much solar energy can be generated.

The Earth is tilted on its axis, so not all areas receive equal amounts of sunlight at any given time. When the Northern Hemisphere tilts toward the Sun, the upper part of the globe gets more Sun exposure and observes spring and summer, while the Southern Hemisphere is colder and darker. When the southern half of the planet tilts toward the Sun, areas on Earth’s southern half get more Sun and the upper half gets less.

Areas near the equator get consistent sunlight year-round, so zones closer to it – such as Southern California or the Sahara desert – have more intense solar power than places closer to Earth’s poles, such as Alaska.

Solar cars would also struggle to collect enough sunlight on overcast or rainy days. Even big utilities with huge solar farms have to plan for times when the Sun doesn’t shine.

And drivers need their cars to operate at night. In order for a solar car to run after dark, it would need to use extra energy that it collected during the day and stored in a battery. Solar panels and batteries increase the weight of the car, and heavier cars need more power to run.

Read More: The Sunswift 7 – a solar-powered car – sets a new Guinness World Record for distance and top speed

Researchers are working to design solar cars that are more suitable for everyday use. For this to happen, designers will need to make solar panels more efficient at converting sunlight to energy and design solar panels that are more suitable for cars. It also will be critical to make solar systems for cars cheaper, so average buyers can afford them.

For now, the closest option to a solar car is an electric vehicle that’s charged at home or at a charging station. Depending on how that electricity is generated, some of the energy that flows into these cars is likely from solar panels, wind turbines, hydropower dams or other renewable sources. And that share will rise as states work to switch to clean energy over the next several decades. If you’re driving or riding in an electric car, you might be travelling on solar power right now.

• Chen Liu is an Associate Professor of Electrical & Computer Engineering, Clarkson University
• This article first appeared in The Conversation

I’m a professor who studies batteries and electrochemistry. To understand why batteries come in many different sizes and shapes – and serve many purposes – look to the past, at how batteries originated and how they have developed over the years.

The first batteries were made in the 1800s, and they were quite simple. One of the first demonstrations was a series of metal discs soaked in brine, which Italian scientist Alessandro Volta found created an electric current. The first lead-acid battery was made of a few pieces of lead in a jar of sulfuric acid. The modern versions are not that different. They’re just easier to manufacture and contain various additives to improve performance.

In all cases, batteries perform in the same manner: a voltage difference between two dissimilar electrodes produces an electric current, which can be discharged to power a device. Rechargeable batteries can then reverse this current to charge back up. Inside the battery, the electric current is accompanied by the flow of ions through a liquid, the electrolyte.

The passage of each electron in the current is accompanied by the transport of one ion through the electrolyte. Electrodes that can store more ions lead to batteries that can hold more charge and therefore last longer on a single charge. Electrodes that are engineered for faster ion storage lead to batteries that can discharge faster, for high-power applications. Lastly, being able to charge and discharge many times without degrading leads to batteries with long lifetimes.

Lead-acid batteries

The lead-acid battery was the first rechargeable battery invented back in 1859 by Gaston Plante, who experimented with lead plates in an acidic solution and found that the flow and storage of electric current could be reversed.

A lead-acid battery has to be big enough to provide enough charge to start a car. It also has to be usable in cold climates and last many years. Since the electrolyte is a corrosive acid, the external casing has to be tough to protect people and car parts from any possible harm. Knowing all this, it makes sense that modern lead-acid batteries are blocky and heavy.

Alkaline batteries

On the other hand, household devices like calculators and digital scales can afford to use smaller batteries because they don’t require a lot of charge. These are primarily non-rechargeable alkaline batteries that have been used for decades. The standardized cell sizes are AAAA, AAA, AA, C and D, as well as button and coin cells and many others. The sizes are related to how much charge they store – the bigger the battery, the more it holds – and the sizes of the devices they power.

Sometimes, you may find alkaline batteries sold in rectangular shapes, like common 9-volt batteries, but open the outer casing and you’ll find that they are simply a few cylindrical cells connected together inside. Cylindrical batteries have been around so long and used so widely that it just doesn’t make sense for the companies to manufacture anything different – it would require an investment to change their manufacturing facilities, something they’d rather not do.

Lithium-ion batteries

Nickel-cadmium batteries were the first widely used rechargeable batteries for household electronics and were popular through the end of the 20th century. But they had their pitfalls. Cadmium is very toxic, and the batteries suffered from a “memory effect,” which decreased their lifetime.

For many decades, lithium was studied for potential use in rechargeable batteries because of its unique properties as a lightweight metal that stores a lot of energy. Sony first commercialized the lithium-ion battery in 1991.

The company made cylindrical cells because these were the easiest to manufacture. In the 1990s, Sony was making lots of camcorders and tapes and thus had lots of equipment for roll-to-roll manufacturing. It was natural to repurpose this equipment to produce rolls of battery electrodes, which are made by casting films on sheets of copper or aluminium and then rolling them up into a “jelly roll” cylinder.

The thick casing of these cylindrical cells is mechanically strong, and to add another layer of safety they have a pressure relief valve. Very quickly, these early lithium-ion cells took over the portable electronics market, especially for laptops and cellphones, because they stored more energy and lasted longer than nickel-cadmium rechargeable batteries.

Factors that shape batteries

Batteries are made in certain sizes and shapes for reasons of cost and manufacturability, but in other cases because of legacy manufacturing processes. Market demand also plays a role.

For example, electric vehicles didn’t take off until Tesla started making cars using cylindrical lithium-ion battery cells rather than the rectangular pouch or prismatic cells other EV makers have used. Pouch and prismatic cells can be packed closely together, but because cylindrical cells were already being mass-produced for portable electronics, Tesla was able to make lower-cost EVs in the 2010s.

Read More: Lithium-ion batteries don’t work well in the cold − a battery researcher explains the chemistry at low temperatures

What shapes and sizes batteries will take in the future depends not only on how much energy they store, but also on market economics – how easy it is to make each type of cell, how much it costs to make them and what they’re used for. And those factors are a mix of innovation and history.

• Wesley Chang is an Assistant Professor of Mechanical Engineering and Mechanics, Drexel University
• This article first appeared in The Conversation