I come from Trinidad and Tobago. As a country that was once colonized by the British, I am wary of the ways that inequalities between the global north and global south risk being perpetuated in the digital age. When we consider the lack of inclusion of the global south in discussions about artificial intelligence (AI), I think about how this translates to an eventual lack of economic leverage and geopolitical engagement in this technology that has captivated academics within the industrialised country I reside, the United States. As a scientist, I experienced an early rite of passage into the world of Silicon Valley, the land of techno-utopianism, and the promise of AI as a net positive for all. But, as an academic attending my first academic AI conference in 2019, I began to notice inconsistencies in the audience to whom the promise of AI was directed.

Numerous parties are calling for the democratisation of AI, but the phrase is used to refer to a variety of goals, the pursuit of which sometimes conflict. This paper identifies four kinds of AI democratisation that are commonly discussed: (1) the democratisation of AI use, (2) the democratisation of AI development, (3) the democratisation of AI profits, and (4) the democratisation of AI governance. Numerous goals and methods of achieving each form of democratisation are discussed. The main takeaway from this paper is that AI democratisation is a multifarious and sometimes conflicting concept that should not be conflated with improving AI accessibility. If we want to move beyond ambiguous commitments to democratising AI, to productive discussions of concrete policies and trade-offs, then we need to recognise the principal role of the democratisation of AI governance in navigating tradeoffs and risks across decisions around use, development, and profits.

It's been decades since algorithmic trading transformed Wall Street with its high-frequency trading, and years since the financial services industry began to integrate artificial intelligence in areas such as fraud detection, lending decisions and robo-advisory services. Yet the recent explosion of generative AI tools like ChatGPT – providing human-like text on seemingly any subject and any style so successfully it easily conquers the vaunted Turing Test – has opened the floodgates of possibilities. The advent of such a power language processor like ChatGPT – open source and available for public use – threatens to upend various parts of the financial services industry, spanning beyond areas such as chat bots and robo-advisors to even the workforce needed in something as skilled as coding. As artificial intelligence reaches a crucial tipping point – and AI bias lingers – whether the proper private and public controls are put in place ahead of the technology's dizzying progress becomes even more urgent yet challenging. In a recent Nvidia survey, 78 percent of financial services companies said they use at least one kind of artificial intelligence tool.

I have a grayish dual position regarding generative art and, well, basically, generative creativity. One view is extremely cynical, and the other perspective is hopeful. I wrote earlier about this topic here (note: a bit gloomy). Let me start with the cynical view, hyperbolized for ease of communication. I see this as a big tech effort to lower tech wages, reduce negotiation positions of creative workers, push the commoditization of art, create a new scaleable consumer market, and more holistically drive society towards transhumanism.

Machine learning (ML) and artificial intelligence (AI) were once concepts relegated to only the most optimistic observers, much like self-driving electric vehicles and smartphones once were. But if it isn't obvious, the times have changed. Today, ML and AI--along with the immensely powerful data collection and analytics tools that power those processes--are a mainstay of modern life. Every day, people interact with products and services powered by some of the world's most ground-breaking technology. In the financial sector specifically, ML and AI present an enormous opportunity to institutions to revolutionise their businesses and generate both top- and bottom-line results.

Although brands strive to optimise the delivery of their product and services, customer experience is a moving target that is much harder to quantify and measure. When selling was done in-person, this was not an issue, because humans are extremely good at gauging customer's expectations and accommodating to all the different customer needs. In the wake of the post-pandemic era, digital commerce is now the new normal. All businesses are shifting online devoid of human touch. This digital shift is not only a response to containing the spread of COVID, but rather a strategic move to operate one's business more efficiently.

As AI models become increasingly refined, organisations are starting to notice the diverse range of solutions they can provide across not just data science teams but all departments of a company. Despite this, scaling AI solutions across a company is an extremely expensive and complex venture that most firms would struggle to see a return on investment from. In a market where AI has so much to offer but with such large costs, Peltarion is striving to bridge the gap enterprises face when realising AI solutions in terms of resources and knowledge. AI News joined Peltarion CEO Luka Crnkovic-Friis and operational AI expert Johan Hartikainen to discuss how the company's cloud-based software platform is helping to solve this catch-22 situation affecting the industry. AI News: In another interview you described Peltarion as doing for AI what WordPress did for HTML coding, would you still consider this an accurate analogy?

In the final article of a three part series* focusing on what AI startup founders are doing to navigate the fast growth AI industry, Information Age spoke to 16 founders of some the UK's leading AI startups and scaleups to understand their artificial intelligence trends for 2021, including its growing use in a variety of industries, the importance of data and talent, the impact of Covid-19 and the democratisation of AI. "Fundamentally, AI is becoming more accepted and utilised across all areas of life and people are experiencing the benefits," says Mark Nicholson, CEO, Vivacity Labs. This growing use and acceptance of AI has been driven, in part, by the disruption caused by the Covid-19 pandemic. "More and more businesses that are not fully digitalised want to undergo a digital transformation. The healthcare sector, including drug discovery in the pharmaceutical industry, has experienced a tremendous challenge over the last year. But, AI is increasingly being used in the medical sector to help tackle the virus "by analysing and interpreting data on the virus's spread," according to Dr Alex Young, founder and CEO at Virti. "It is also being used in healthcare to help with treatment and medical training.

The world of AI has been shaken by Google's dismissal of AI Ethicist Dr Timnit Gebru last week. This behaviour is emblematic of the self-centred attitudes of major tech companies which also results in lack of commitment to democratisation of technology. With Facebook, Amazon and Apple also in the spotlight for allegedly creating a monopoly, the time has come for SMEs to re-revaluate their AI providers. The tech industry has experienced a meteoric rise this millennium, growing into one of the world's largest industries, with investment increasing by £3.1 billion in 2019 alone. Amazon, Apple, Facebook and Google have a combined worth of $4 trillion, giving them unprecedented power over the marketplaces they facilitate.

In the previous articles I looked at how data is being considered as currency today, and that to start to realise value from data it first needs to be sorted. I discussed how challenging this was not only because of the large quantities of data but because of something called'data bias' which can skew our perception of the results. We are creating more data than ever before in our history, with most ending up as'data exhaust' (a waste by-product that is not used or stored). Of the data we do store most is archived, never used and its value never realised. Alongside this we have a scarcity and lack of diversity in the people that sort our data and extract the business value and insights we need.