Today, AcademicID has announced the official launch of its state-of-the-art, AI-powered platform designed to revolutionise the research process, help academics simplify their workload and provide new data insights to institutions and funders. Minerva, AcademicID's AI research assistant, is a tool to streamline knowledge discovery and save time on administrative tasks. With the ability to respond intelligently and swiftly to requests, Minerva can comb through more than 200 million academic papers to provide detailed answers and relevant findings. Minerva's ability to return relevant academic research ensures that all responses are quickly verifiable – a defining feature not available on other chatbots such as ChatGPT – ensuring academics can more confidently utilise the incredible power that latest-generation AI technologies offer. The platform's advanced AI technology also allows academics to find and stay across the latest research, source collaborators and use advanced data analytics to track their careers quickly and accurately.

Artificial Intelligence (AI) is a disruptive and fast-moving field whose developmental trajectory is accelerating rapidly. In fact, the number of publications in this space has been rising dramatically in recent years. Stanford's annual Artificial Intelligence Index Report shows that the number of AI publications has increased from 162,444 in 2010 to 334,497 in 2021 [1]. If you are working in the field of AI, you have probably also noticed the shortening intervals between major industry advances such as OpenAI's DALL·E 2, GPT-3 and ChatGPT, or DeepMind's AlphaFold. Those are just some examples that captured the attention of both the general public and the tech industry as they were extensively reported on and widely circulated on social media.

Three people with paralysis of all four limbs used their thoughts to steer a wheelchair through a cluttered room with a reasonably high level of accuracy. This suggests people with paralysis could move independently through certain rooms, but the technology may not be advanced enough to navigate a busy street. A range of different researchers have previously used two main strategies to test mind-controlled wheelchairs on non-disabled people. The first involves a person focusing on a flickering light in a particular location. This generates brain signals that an artificial intelligence translates into wheelchair movements towards that location, but this approach often leads to eyestrain.

Among the various Data Science areas of specialization, recommender systems and personalization is an extremely popular one. For those new to the field or yet to choose an area of focus, do check out this brief introduction by Aashish Nair on Towards Data Science.

This book is a must-know for those working as health data scientists.

Machine learning and Artificial Intelligence models have gained promising results in recent years. The major factor behind their success is the availability and development of vast datasets. However, regardless of how many terabytes of data you have or how skilled you are at data science, machine learning models will be useless and even dangerous if you can't make sense of data records. A slice is a collection of data samples with a common feature. For example, in a picture dataset, photographs of antique vehicles make up a slice.

While a vast majority of research efforts today are preoccupied solely with ML models and algorithms, the data itself tends to be secondary and is treated as fixed. This claim is potentially detrimental – there's a big risk of favoring theory over practice as the models are becoming more divorced from the ground truth. There's a need to combat this trend by providing incentive and information to researchers and practitioners alike to work with the data instead. All of the articles are available on the Proceedings page here, as are Datasets and Benchmarks and the Data-Centric AI Workshop respectively. It's extremely important to measure the quality of data sets; however, there's currently no universally agreed-upon method of how to do it.

Artificial intelligence (AI) is primarily a math problem. We finally have enough data and processing capacity to take full advantage of deep neural networks, a type of AI that learns to discover patterns in data, when they began to surpass standard algorithms 10 years ago. Today's neural networks are even more data and processing power-hungry. Training them necessitates fine-tuning the values of millions, if not billions, of parameters that describe these networks and represent the strength of artificial neuron connections. The goal is to discover almost perfect values for them, known as optimization, but training the networks to get there is difficult.

Federated learning is a new way to train artificial intelligence models with data from multiple sources while maintaining anonymity. This removes many barriers and opens up the possibility for even more sharing in machine learning research. The latest results published in Nature Medicine show promising new research wherein the federated learning models build powerful AI models that can be generalized among healthcare institutions. These findings are currently for the healthcare industry. It shows that further down the line, it could have a significant role in energy, financial services, and manufacturing applications.

Today, Artificial Intelligence (AI) can drive effective solutions to lots of marketing challenges. It can help you optimize your advertising budget, personalize your customers' experience, suggest the most accurate attribution model, boost your marketing analytics, and even generate original brand content, including images and advertising slogans. However, AI is evolving so fast these days that even the most advanced marketing teams can find it challenging to follow all the research advances in the field and fail to keep track of all possible business applications for the novel AI approaches. If you're an intermediate or advanced practitioner, we've created premium education to help you and your team master recent breakthroughs in applied AI for marketing. Click here to purchase our AI in marketing research summaries.