Although prototype-based explanations provide a human-understandable way of representing model predictions they often fail to direct user attention to the most relevant features. We propose a novel approach to identify the most informative features within prototypes, termed alike parts. Using feature importance scores derived from an agnostic explanation method, it emphasizes the most relevant overlapping features between an instance and its nearest prototype. Furthermore, the feature importance score is incorporated into the objective function of the prototype selection algorithms to promote global prototypes diversity. Through experiments on six benchmark datasets, we demonstrate that the proposed approach improves user comprehension while maintaining or even increasing predictive accuracy.

This paper mainly combines another MOO algorithm and MOO-MTL, and improves the results from last year NIPS paper Multi-objective MTL. The technical contribution for MOO and MTL is limited since this paper just borrow the MOO optimization method directly from reference [24] and reference [29]. Nevertheless, I think this paper has a potential impact in MTL community since I do not find any previous paper achieves similar effects, which could guide people get different high-quality MTL results without random trails. Quality: below the average The quality of the paper is below the bar. Some important part is missing and lack of deep analysis.

Artificial intelligence that scans GP records to find hidden patterns has helped doctors detect significantly more cancer cases. The rate of cancer detection rose from 58.7% to 66.0% at GP practices using the "C the Signs" AI tool. This analyses a patient's medical record to pull together their past medical history, test results, prescriptions and treatments, as well as other personal characteristics that might indicate cancer risk, such as their postcode, age and family history. It also prompts GPs to ask patients about any new symptoms, and if the tool detects patterns in the data that indicate a higher risk of a particular type of cancer, then it recommends which tests or clinical pathway the patient should be referred to. C the Signs is used in about 1,400 practices in England – about 15% – and was tested in 35 practices in the east of England in May 2021, covering a population of 420,000 patients.

The emergence of effective natural language processing tools such as ChatGPT means it's time to begin understanding how to harden against AI-enabled cyberattacks. The natural language generation capabilities of large language models (LLMs) are a natural fit for one of cybercrime's most important attack vectors: phishing. Phishing relies on fooling people and the ability to generate effective language and other content at scale is a major tool in the hacker's kit. Fortunately, there are several good ways to mitigate this growing threat. A leader tasked with cybersecurity can get ahead of the game by understanding where we are in the story of machine learning (ML) as a hacking tool.

If you have been using twitter recently I bet that from the last 10 tweets 5 of them are linked to AI and the rise of chatGPT. Looking at those tweets AI tools are going to change the world as we know it. This is even more significant in the case of Education and in particular in Higher Education where most of the traditional methods of assessments are based on essay that consists on pieces of written work where students have to answer questions in a specific number of words. A lot of messages that I hear from institutions, and normally from traditional institutions, is that we need to ban chatGPT as this is a danger and a temptation to student to cheat and create this contractual cheating situation were students are submitting work that is not they original work. I think that this view is completely wrong and it is just a way to avoid the problem without thinking first why there is a problem and second what is actually AI and the possible benefits that can bring to education, learning outcomes and yes to assessments.

An important part of the process is to bring in people from across disciplines, even if they have conflicting perspectives. A few years back, experts and pundits alike were predicting the highways of the 2020s would be packed full of autonomous vehicles. One glance and it's clear there are still, for better or worse, mainly human drivers out there on the roads, as driverless vehicles have hit many roadblocks. Their ability to make judgements in unforeseen events is still questionable, as is the ability of human riders to adapt and trust their robot drivers. Autonomous vehicles are just one example of the greater need for human-centered design, the theme of the recent Stanford Human-Centered Artificial Intelligence fall conference, in which experts urged more human involvement from the very start of AI development efforts.

Happy New Year everyone, Maybe last year was full of crap for someone or full of new achievements for someone, whatever it is everyone is planning a new start for the new. Same goes for me as well. Last year was really very tough year for me but at the end of the year I moved to United Kingdom, which I always wanted to. But it hit me with many uncertainties like getting a new job, settling up and many stuff. So, I was planning for a new head start to the dream career I always wanted to pursue that to become a Machine learning Engineer. And I want to share my journey step by step whatever I am following or planning to do for the next year, what will I achieve to everyone.

Neural networks are a type of machine learning algorithm that are inspired by the way the human brain works. They are used to recognize patterns and make decisions based on input data. Although there are several different types of neural networks, and each has its own unique characteristics and applications. Let's discuss the three most prominent ones, starting with, An artificial neural network (or ANN) is a type of machine learning algorithm that is inspired by the way the human brain works. It is made up of a network of artificial "neurons," which are inspired by the way biological neurons in the brain work.

There are several reasons why you might want to write articles. Writing can be a great way to express your thoughts and ideas, and can be a therapeutic and creative outlet. Additionally, writing can be a way to share your expertise and knowledge with others, informing and educating readers on a wide range of topics. Writing articles can also be a way to showcase your writing skills and potentially help you to build a reputation as an expert in your field. Furthermore, writing articles can be a fulfilling and rewarding experience, as it allows you to share your thoughts and ideas with others and potentially make a positive impact on their lives.

Attention is a cognitive and behavioral function that gives us the ability to concentrate on a tiny portion of the incoming information selectively, which is advantageous to the task we are attending. It gives the brain the ability to confine the volume of its inputs by ignoring irrelevant perceptible information and selecting high-value information. When we observe a scene with a specific important part related to the task we are doing, we extract that part to process more meticulously; we can learn to focus on those parts more optimally when those scenes appear again. According to J. K Tsotsos et al. [1], the attention mechanism can be categorized into two classes. The first category is bottom-up unconscious attention -- saliency-based attention -- which is stimulated by external factors.