Related Jobs
View all jobsFounding Machine Learning Engineer | London | Audio/Vision
AI / Machine Learning Engineer
Senior Machine Learning Engineer - Computer Vision
ML Engineer
Computer Vision Development Engineer
Get the latest insights and jobs direct. Sign up for our newsletter.
Industry Insights
Discover insightful articles, industry insights, expert tips, and curated resources.
Shifting from Academia to the AI Industry: How Researchers Can Harness Their Skills to Drive Commercial Artificial Intelligence
Artificial intelligence (AI) has advanced from a specialised academic pursuit to a transformative force in almost every sector—from healthcare diagnostics and autonomous vehicles to recommendation systems and creative generative models. As AI technologies continue to grow in complexity and impact, companies are looking for talent that combines deep theoretical knowledge with the ingenuity to solve real-world challenges. Increasingly, PhDs and academic researchers fit this profile perfectly. This guide will help you map out the transition from academia to industry in artificial intelligence. Whether you specialise in reinforcement learning, computer vision, natural language processing, or another AI discipline, you’ll find actionable advice on how to translate your academic strengths, adapt to commercial constraints, and excel in roles where your research insights can revolutionise products, services, and user experiences.
The Ultimate Glossary of AI Terms: Your Comprehensive Guide to Artificial Intelligence
Artificial Intelligence (AI) is transforming the modern workforce and daily life at an unprecedented pace. From healthcare to finance, AI-driven solutions are helping organisations streamline processes, enhance decision-making, and offer innovative products and services. As a result, AI jobs are in high demand, offering lucrative salaries and exciting career paths for those with the right skill set. Whether you’re starting your journey toward an AI career or you’re a seasoned professional aiming to stay on top of the latest developments, a strong command of AI terminology is essential. This glossary of key AI terms will help you navigate important concepts, from fundamental machine learning techniques to advanced topics like deep learning and ethical AI. By familiarising yourself with this comprehensive list, you’ll be better equipped to discuss AI trends, contribute to innovative projects, and identify new opportunities in one of tech’s fastest-growing fields.
Diversity & Inclusion in AI Jobs: Building a More Equitable Workforce for Recruiters and Job Seekers
Artificial Intelligence (AI) is rapidly reshaping how we live, work, and interact with technology. From virtual assistants in our homes to complex machine learning algorithms powering healthcare diagnostics, AI is becoming increasingly integrated into our daily lives. As the field expands, AI systems and solutions are touching more people and influencing more industries than ever before. Despite the vast potential of these technologies, one critical area that requires urgent attention is diversity and inclusion (D&I) within AI teams and organisations. The current state of diversity in AI can be best described as a work-in-progress. For years, the technology sector as a whole has struggled to achieve balanced representation across gender, race, socioeconomic status, and other underrepresented groups in tech. While there has been some improvement—thanks to awareness campaigns, diversity programmes, and mentorship initiatives—recent studies still reveal a stark gap in the presence of women, ethnic minorities, people with disabilities, and other marginalised communities in AI roles. According to various reports, women represent only a fraction of the AI workforce, and people from Black or other ethnic minority groups remain disproportionately underrepresented in tech leadership positions. The lack of diversity in AI reflects broader inequalities within STEM (science, technology, engineering, and mathematics) fields, and these issues can have direct consequences on the products and services we create. Why does this matter so much? AI systems are often trained on large datasets that can inadvertently carry biases. If the teams building AI are not representative of the society their products serve, it can lead to entrenched biases—sometimes in unexpected ways. For example, facial recognition software has historically struggled to accurately recognise non-white faces. Chatbots and language models have, at times, echoed prejudices lurking in their training data. When AI products and services fail to consider the full spectrum of users, the results can undermine public trust, stall innovation, and cause real-world harm. On the flip side, the benefits of inclusive teams for innovation and product design are considerable. Diverse and inclusive environments tend to foster a broader range of perspectives, ensuring that potential blind spots are addressed early in the AI development process. This leads to more robust, user-friendly products that cater to a wider audience. Multiple studies have shown that companies prioritising diversity are not only more innovative but also more profitable. By embracing underrepresented groups in tech, employers can tap into a broader talent pool, spark creative solutions, and boost employee satisfaction. But how do we go about creating more inclusive AI workplaces? In this article, we will explore the barriers to entry that many underrepresented groups face, examine successful D&I initiatives shaping the industry, and provide practical advice on how job seekers and employers alike can champion diversity in AI. Our goal is to illuminate pathways to a more equitable AI workforce, one where talent and merit thrive unhindered by systemic barriers. By taking deliberate steps to break down these barriers and actively promote inclusivity, we can ensure that AI technology reaches its full potential for everyone. Whether you are an aspiring AI professional, a hiring manager, or simply curious about how the tech industry is evolving, the following insights will help you better understand why diversity in AI is so vital. Moreover, you’ll learn about concrete steps you can take—or encourage your company to take—to nurture a thriving, diverse workforce. Ultimately, creating an inclusive environment is not just a corporate social responsibility; it is the key to unlocking the next wave of AI-driven innovation and ensuring the technology serves all communities in a fair and ethical manner.