Technology AI Why does AI suck at making clocks? 'Telling time is a very human thing.' Breakthroughs, discoveries, and DIY tips sent every weekday. The premise is simple: all the major AI models on the market are asked to code a clock, and you get to see the results. The numbers seem to be consistently in the wrong place, and sometimes are outside the clock itself.

Most existing dialogue corpora and models have been designed to fit into 2 predominant categories : task-oriented dialogues portray functional goals, such as making a restaurant reservation or booking a plane ticket, while chit-chat/open-domain dialogues focus on holding a socially engaging talk with a user. However, humans tend to seamlessly switch between modes and even use chitchat to enhance task-oriented conversations. To bridge this gap, new datasets have recently been created, blending both communication modes into conversation examples. The approaches used tend to rely on adding chit-chat snippets to pre-existing, human-generated task-oriented datasets. Given the tendencies observed in humans, we wonder however if the latter do not \textit{already} hold chit-chat sequences. By using topic modeling and searching for topics which are most similar to a set of keywords related to social talk, we explore the training sets of Schema-Guided Dialogues and MultiWOZ. Our study shows that sequences related to social talk are indeed naturally present, motivating further research on ways chitchat is combined into task-oriented dialogues.

Nothing can beat human thinking in any way. Most programming experts are on the verge to create such a computer system that can think and reason without any human intervention. Basically, they are working on cognitive artificial intelligence that can process human thought into a computerized model. A cognitive computer is a system that learns at scale, reasons with purpose, and interacts like humans on a natural basis. Instead of being programmed, these systems work through learning and reasoning from their interactions with human beings.

There are many examples that could be given to demonstrate the benefits of technology and Artificial Intelligence in supporting the rehabilitation of people who have health issues. To start with, there is the project being carried out in the intensive care unit (ICU) of the Hospital del Mar in Barcelona, where people admitted to hospital can start their recovery by'taking a ride' on a bicycle without actually leaving their bed, in a process that combines physical exercise and virtual reality. This idea consists of an ergometric bicycle that allows bedridden patients to do physical activity similar to cycling, combined with virtual reality to start their rehabilitation process in an "early and enjoyable" manner. The system allows the patient to pedal even when lying in bed and adapts to their physical capacity, even having the capacity to have them move their legs passively and thus mobilising the muscles when the patient does not yet have the capacity to do so themselves. In addition, the bicycle is combined with a 360-degree reality glasses system, which allows patients to feel as if they were actually cycling through different areas of the city of Barcelona.

Tesla CEO Elon Musk has been promising customers a driverless vehicle since at least 2016. While the company hasn't delivered on that promise, Tesla lets thousands of employees and customers try new and unfinished driver assistance features on public roads in the U.S. through a program called Full Self Driving Beta, or FSD Beta. Only Tesla owners who have the company's premium FSD driver assistance system installed in their cars can join the FSD Beta program. Owners must then obtain a high driver-safety score, as determined by Tesla software that monitors their driving habits, and maintain it to keep FSD Beta access. No safety certification or professional training is required.

Important note on nomenclature: While often referred to as "AI" registers, the more appropriate term should be "Algorithmic Decision System" (ADS) registers. A register is an official record of information. In its simplest form, it can provide visibility over algorithmic systems in use, which is the most basic form of transparency. It can also be designed to enable meaningful transparency that meets stakeholder information needs. It's the latter use case that I explore throughout this article. Meaningful transparency is an important concept in AI ethics.

IT is facing significant talent shortages, and new HR AI talent recruiting systems are touted as being able to help. How do these systems work, and where to apply them? IT is facing significant talent shortages, and new human resources AI talent recruiting systems are touted as being able to help. How do these systems work, and are they effective? The purpose of artificial intelligence hiring and talent scouting systems is to reduce the amount of work that HR or IT conducts in the activities of talent seeking, candidate evaluation and hiring.

Explainable AI (XAI) fully describes an AI model, its expected impact and any potential biases. It helps you understand the steps taken by an AI technique to arrive at a decision. In this article, we will take a look at XAI in detail and explore how you can implement it in your organisation. "About half (46%) of South African companies indicate that they are already implementing AI within their organisations." Why is explainable AI important for your business?

This might be the first time you hear about Explainable Artificial Intelligence, but it is certainly something you should have an opinion about. Explainable AI (XAI) refers to the techniques and methods to build AI applications that humans can understand "why" they make particular decisions. In other words, if we can get explanations from an AI system about its inner logic, this system is considered as an XAI system. Explainability is a new property that started to gain popularity in the AI community, and we will talk about why that happened in recent years. Let's dive into the technical roots of the problem, first.

Councils are quietly scrapping the use of computer algorithms in helping to make decisions on benefit claims and other welfare issues, the Guardian has found, as critics call for more transparency on how such tools are being used in public services. It comes as an expert warns the reasons for cancelling programmes among government bodies around the world range from problems in the way the systems work to concerns about bias and other negative effects. Most systems are implemented without consultation with the public, but critics say this must change. The use of artificial intelligence or automated decision-making has come into sharp focus after an algorithm used by the exam regulator Ofqual downgraded almost 40% of the A-level grades assessed by teachers. It culminated in a humiliating government U-turn and the system being scrapped.