We’ve all heard the scare stories. The availability of endless data will allow organisations to become less reliant on the human workforce. Artificial Intelligence (AI) is going to be smarter than humans. And automation will take away lots of our jobs. How much of this is really true though? Despite advances in these technologies, like conversational AI, they’re just tools to be used in the endeavour of making our lives easier and organisations more productive. But even a tool with contextual and conversational capabilities can’t provide the unique flexibility of human touch and true ingenuity that we all desire and

ChatGPT, the dialogue-based AI chatbot capable of understanding natural human language, has become another icon in the disruptor ecosystem. Gaining over 1 million registered users in just 5 days, it has become the fastest growing tech platform ever. ChatGPT generates impressively detailed human-like written text and thoughtful prose, following a text input prompt. In addition, ChatGPT can write and hack code which is a potential major headache from an infosec point of view and has set the Web3 community on fire. Following the hype around ChatGPT, the race is now on between OpenAI's Chat GPT and Google's LaMDA to be the market leading NLP search tool for users and corporations moving forward.

The past year has seen artificial intelligence (AI) become a dinner-table topic of conversation around the world, thanks to bots such as ChatGPT, which dazzles users with its ability to compose lifelike text and even computer code. But what happens when AI makes wrong decisions? Bias – and gender bias in particular – is common in AI systems, leading to a variety of harms, from discrimination and reduced transparency, to security and privacy issues. In the worst cases, wrong AI decisions could damage careers and even cost lives. Without dealing with AI's bias problem, we risk an imbalanced future – one in which AI will never reach its full potential as a tool for the greater good.

In the face of a challenging macroeconomic climate, the UK digital advertising market remains remarkably strong, expected to reach $35.43bn by the end of this year. With advertisers increasingly relying on digital channels for driving brand awareness and sales, platforms like Connected TV (CTV), digital audio and digital out-of-home (DOOH) are picking up a larger slice of the ad spend pie. In contrast to just a few years ago, this investment would have traditionally been allocated to offline media. This is not to say that the industry is without its problems however. The economic situation, amongst other geopolitical pressures, is having an adverse effect on the sector, forcing media planners to think more short-term and reactively.

Is conversational AI a blessing or curse for data? If you follow the tech industry, you’ve heard about ChatGPT. Whether you think it’s the future of chatbot technology or you’re erring on the side of caution, if you know about it, you’re bound to have an opinion. As Google confirms it’s launching a rivalling service, interacting with AI will soon become commonplace in our personal and professional lives. But what does that mean for data and analytics? Here, Jonathan Hedger, co-founder of the UK’s only data jobs board, Only Data Jobs, explores. Launched late in 2022, ChatGPT has quickly become

Conversational AI is playing an increasing role in customer service contact centres. It can greet customers, handle routine requests in a conversational manner, and more accurately route interactions to the service agents who can best assist. But when a customer reaches out to a contact centre, they are often frustrated because they have unsuccessfully tried to solve their problem online, and they expect their request to be met with empathy and urgency. Irritation can take over if the user reaches an AI bot when they need a human conversation; or if they have to wait for a human when an AI could resolve the issue more efficiently. When seeking immediate answers and information, 36% of customers choose self-service chat or a virtual agent.

From 2001: A Space Odyssey and Ex Machina to Wall-E and Her, artificial intelligence has reliably been a subject of fascination in modern culture. But AI is no longer a thing of imagination, books or film scripts – it is already playing a pivotal role in both our professional and personal lives. And when it comes to the capability of this next-generation technology, we are now on the precipice of an exponential leap. The potential impact of AI on our lives cannot be understated, so the growing AI skills gap must be addressed if we are to ensure that businesses are prepared to take this jump. AI has already transformed the way we interact with banks, how we shop and how we manufacture.

Propelled by the rapid development of innovative technologies and agribusiness-tech partnerships, modern farming is on the verge of the kind of digital transformation process seen across many other industries. In fact, research predicts that by 2026, the AI in agriculture market will grow at over 25% per year to reach a value of $4 billion. According to the same study, this impressive acceleration in the adoption of AI is due to the "increasing implementation of data generation through sensors and aerial images for crops, increasing crop productivity through deep learning technology, and government support for the adoption of modern agricultural techniques." But where is this tech-led innovation being focused? Smart farming, for example, is an autonomous end-to-end system that can gather and process key datasets to give actionable insights.

The slightest mention of AI and machine learning (ML) was enough to strike fear into the hearts of many not so long ago. To be fair it's not surprising, particularly given the cultural references we've been fed over the years – see Skynet (Terminator), Hal (2001: A Space Odyssey), and Ava (Ex-Machina) – it's little wonder there's been a smidgen of anxiety. However, in recent times, technologists have started to acknowledge that AI and ML are actually good at automating the laborious processes we as humans and businesses can't be bothered with – most of the time they do this more accurately too. The question still remains though, do we really have anything to worry about when it comes to automating our working lives via ML and AI? Taken on the findings of a recent PwC report, there's still some substantial negativity among the general population when it comes to automation, with 60% of people believing that it will take their job (and there are further concerns raised when you start to mention the AI elements). That is really two questions in one though: can we automate everything, and can we make that a full end-to-end process?

Current software documentation practices don't adequately serve developers. The ultimate goal of software documentation is to help developers find and fix code quickly and efficiently. Still, in most cases, code comments are difficult to understand, incomplete, out of date and untrustworthy to many developers, often resulting in significant additional work and unintended business risks. Traditionally, supplying detailed documentation and comments in code can help developers quickly get the context surrounding the code they are working on, resulting in increased productivity. While documentation and comments are an important part of software engineering, poor or insufficient documentation is a widespread problem that can ultimately create more problems for developers and negatively impact the business.