Well, it's hard not to like Brent Spiner, the nice Jewish boy from Houston who grew up to write a funny, self-mocking semi-fictional autobiography as well as star in a hit TV series. He calls his book, Fan Fiction, a "mem-noir" where an actor, conveniently named Brent Spiner, on the third season of a modest hit conveniently called "ST:TNG" is being stalked by a someone purported to be Lal, Data's short-lived robot daughter. The hapless actor finds it is as if he is living in a Raymond Chandler novel. Brent reflects on his life and how he got to this point in his career as he tries to go about shooting episodes, going to parties at the Roddenberrys, signing autographs at cons, and hanging out with Patrick Stewart, Levar Burton, and Jonathan Frakes. Yet Spiner comes across as a regular guy, grounded and grateful- and amused- at "making it" in Hollywood.

This Interesting Engineering piece highlights how even an AI built to find'helpful drugs', when tweaked just a little, can find things that are rather less helpful. Collaborations Pharmaceuticals carried out a simple experiment to see what would happen if the AI they had built was slightly altered to look for chemical weapons, rather than medical treatments. According to a paper they published in Nature Machine Intelligence journal, the answer was not particularly reassuring. When reprogrammed to find chemical weapons, the machine learning algorithm found 40,000 possible options in just six hours. These researchers had'spent decades using computers and A.I. to improve human health', yet they admitted, after the experiment, that they had been'naive in thinking about the potential misuse of trade'.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 – 28. The process of writing a television show typically involves a writers room and a lot of time, as humans figure out the plot and the dialogue that makes a show work. For the cult classic Stargate science fiction franchise, which spanned three series (SG-1, Stargate Atlantis and Stargate Universe), character and plot development was helmed by Stargate co-creator Brad Wright. In 2021, Wright publicly posted a message on Twitter asking if it was possible for AI to write an episode of Stargate that would appear on SciFi insider site The Companion. None other than Laurence Moroney, AI lead at Google, responded by picking up the gauntlet to try and prove what AI could do.–

Artificial intelligence is defined as systems that do not operate according to a designed algorithm but are able to learn from new data. The fact that European policymakers have turned their eyes to the challenges of applying AI technologies is an important step forward, according to Jokūbas Drazdas, director of UAB Acrux Cyber Service, a Lithuanian IT company specialising in AI and cyber security. Europe is lagging far behind the US and China in the development and deployment of AI. In 2020, only 7% of European companies were using AI systems. The US and China are currently trying to accelerate the use of AI in the public and private sectors.

Predicting the future is a possibility. So when working with AI, we should be aware of these distributions. That is why I think we need multiple AI for different parts of the market and then do an explainable rule-base module for decision making. As a human, our decision-making module should be dynamic and try to maximize profitability based on changing strategies. Here, AI can help clarify the details behind the scenes, which a human can not do most of the time rapidly.

Technology is not showing signs of slowing down any time soon. As we move into cloud computing, big data, natural language processing and artificial intelligence, the employment sector is gearing up for a big boost in the number of opportunities. Organisations such as Google, Microsoft, Facebook and Apple are aggressively hiring people with expertise in these domains, which makes them highly lucrative. Artificial intelligence is particularly on the cusp of a breakthrough. Technologies such as machine learning, neural networks, genetic algorithms and deep learning are receiving a lot of spotlight.

The future of technology is all about Artificial Intelligence. The rapidly growing trends of the industry would open doors to various opportunities and new developments. For instance, voice recognition and motion sensors have become built-in features in most devices and people expect to see more of what is coming. Being a major part of computer studies, Artificial Intelligence primarily focuses on the development of machines, which are programmed to work and think like humans. The technology consists of speech recognition, problem-solving, learning, and planning.

On March 2, 2021, at a virtual forum attended by stakeholders across the entire industry, the Consumer Product Safety Commission (CPSC) reminded us all that it has the last say on regulating AI and machine learning consumer product safety. The CPSC defines AI as "any method for programming computers or products to enable them to carry out tasks or behaviors that would require intelligence if performed by humans" and machine learning as "an iterative process of applying models or algorithms to data sets to learn and detect patterns and/or perform tasks, such as prediction or decision making that can approximate some aspects of intelligence."3 To inform the ongoing discussion on how to regulate AI, machine learning, and related technologies, the CPSC provides the following list of considerations: Do AI and machine learning affect consumer product safety? Do AI and machine learning affect consumer product safety? UL 4600 Standard for Safety for the Evaluation of Autonomous Products covers "fully autonomous systems that move such as self-driving cars along with applications in mining, agriculture, maintenance, and other vehicles including lightweight unmanned aerial vehicles."5

Nowadays, Machine Learning and Deep Learning methods have become the state-of-the-art approach to solve data classification tasks. In order to use those methods, it is necessary to acquire and label a considerable amount of data; however, this is not straightforward in some fields, since data annotation is time consuming and might require expert knowledge. This challenge can be tackled by means of semi-supervised learning methods that take advantage of both labelled and unlabelled data. In this work, we present new semi-supervised learning methods based on techniques from Topological Data Analysis (TDA), a field that is gaining importance for analysing large amounts of data with high variety and dimensionality. In particular, we have created two semi-supervised learning methods following two different topological approaches.