With the House of Lords having reported in the opportunities and threats posed by artificial intelligence (AI), I have asked what my sector – the PR industry – is doing in anticipation of AI's rise. Are the machines really coming for our jobs or will AI free us up from our time-consuming tasks so we can focus on our core strength: creativity. On 16 April, the House of Lords released its Select Committee report on the impact of AI on British life. For context, the definition of AI that it followed was; "Technologies with the ability to perform tasks that would otherwise require human intelligence, such as visual perception, speech recognition and language translation." The impact of AI on jobs, in particular, is something that has caused much discussion in recent years.

Carly -- helps you manage phone calls ETCH -- helps you manage your networks into a searchable database Findo -- Your smart search assistant across email, files & personal cloud Leap -- recommends companies to apply for based on your skills Lomi -- identifies sales leads Mosaic -- helps you write better resumes Newton -- helps you find a dream job Notion -- helps with email overload, organisation and communication Robby -- a better and smarter calendar Stella -- scans for jobs and helps manage your application process Woo -- helps you make smarter decision for your career, anonymously Aloe -- replaces your notes books, diary and meeting preparation material Wade&Wendy -- your career advisor Nudge.ai Brightcrowd -- helps you find meaningful professional connections Capsule.ai Abi --a health assistant which connects people to doctors for quick advice Ada -- can help if you're feeling unwell Airi -- personal health coach Alz.ai -- helps you care for loved ones with Alzheimer's Amélie -- ...

There has been much speculation about the power and dangers of artificial intelligence (AI), but it's been primarily focused on what AI will do to our jobs in the very near future. Now, there's discussion among tech leaders, governments and journalists about how artificial intelligence is making lethal autonomous weapons systems possible and what could transpire if this technology falls into the hands of a rogue state or terrorist organization. Debates on the moral and legal implications of autonomous weapons have begun and there are no easy answers. The United Nations recently discussed the use of autonomous weapons and the possibility to institute an international ban on "killer robots." This debate comes on the heels of more than 100 leaders from the artificial intelligence community, including Tesla's Elon Musk and Alphabet's Mustafa Suleyman, warning that these weapons could lead to a "third revolution in warfare."

All the major economies in the world understand the strategic importance of artificial intelligence in empowering and transforming economic growth in the coming decades. In July 2017, China's State Council laid out an ambitious AI strategic plan to create a domestic 1 trillion yuan (US$147.80 billion) AI industry and make China the world's leading AI innovation centre by 2030. Although too early to say if China will succeed, all the factors seem to be in China's favour, such as market readiness, start-up money, talent, and supportive government policies and budget. Over the past decade, China's vast population has shown great eagerness and willingness to embrace various new technologies. Take the rapid growth of China's smartphone industry as an example: over the past 10 years, it has grown from a single-digit penetration rate to over 50 per cent in 2017.

From building the Deep Blue computer that beat Garry Kasparov at chess to the Watson artificial intelligence (A.I.) that won Jeopardy, IBM has been responsible for some high-profile public demonstrations of A.I. in action. Its latest showcase is less high concept, but potentially far more transformative -- applying machine learning technology to the subject of organic chemistry. As described in a new research paper, the A.I. chemist is able to predict chemical reactions in a way that could be incredibly important for fields like drug discovery. To do this, it uses a highly detailed data set of knowledge on 395,496 different reactions taken from thousands of research papers published over the years. Teo Laino, one of the researchers on the project from IBM Research in Zurich, told Digital Trends that it is a great example of how A.I. can draw upon large quantities of knowledge that would be astonishingly difficult for a human to master -- particularly when it needs to be updated all the time.

Science and reason have invaded our lives, becoming almost inescapable. We have learned to listen to the facts of Science, to accept them as a proven Truth, a basis for all knowledge. We have learned that disbelief in this Truth is absurd, and a kind of intellectual heresy. Ask the person next to you whether they believe in Science, and chances are they'll say yes. Due to our upbringing and education we have learned to accept its facts like Gospel.

The purpose of this paper is to re-investigate the estimation of multiple factor models by relaxing the convention that the number of factors is small. We first obtain the collection of all possible factors and we provide a simultaneous test, security by security, of which factors are significant. Since the collection of risk factors selected for investigation is large and highly correlated, we use dimension reduction methods, including the Least Absolute Shrinkage and Selection Operator (LASSO) and prototype clustering, to perform the investigation. For comparison with the existing literature, we compare the multi-factor model's performance with the Fama-French 5-factor model. We find that both the Fama-French 5-factor and the multi-factor model are consistent with the behavior of "large-time scale" security returns. In a goodness-of-fit test comparing the Fama-French 5-factor with the multi-factor model, the multi-factor model has a substantially larger adjusted $R^{2}$. Robustness tests confirm that the multi-factor model provides a reasonable characterization of security returns.

This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting. We combine this within a distributed framework for off-policy learning in order to develop what we call the Distributed Distributional Deep Deterministic Policy Gradient algorithm, D4PG. We also combine this technique with a number of additional, simple improvements such as the use of $N$-step returns and prioritized experience replay. Experimentally we examine the contribution of each of these individual components, and show how they interact, as well as their combined contributions. Our results show that across a wide variety of simple control tasks, difficult manipulation tasks, and a set of hard obstacle-based locomotion tasks the D4PG algorithm achieves state of the art performance.

Voice conversion (VC) aims at conversion of speaker characteristic without altering content. Due to training data limitations and modeling imperfections, it is difficult to achieve believable speaker mimicry without introducing processing artifacts; performance assessment of VC, therefore, usually involves both speaker similarity and quality evaluation by a human panel. As a time-consuming, expensive, and non-reproducible process, it hinders rapid prototyping of new VC technology. We address artifact assessment using an alternative, objective approach leveraging from prior work on spoofing countermeasures (CMs) for automatic speaker verification. Therein, CMs are used for rejecting `fake' inputs such as replayed, synthetic or converted speech but their potential for automatic speech artifact assessment remains unknown. This study serves to fill that gap. As a supplement to subjective results for the 2018 Voice Conversion Challenge (VCC'18) data, we configure a standard constant-Q cepstral coefficient CM to quantify the extent of processing artifacts. Equal error rate (EER) of the CM, a confusability index of VC samples with real human speech, serves as our artifact measure. Two clusters of VCC'18 entries are identified: low-quality ones with detectable artifacts (low EERs), and higher quality ones with less artifacts. None of the VCC'18 systems, however, is perfect: all EERs are < 30 % (the `ideal' value would be 50 %). Our preliminary findings suggest potential of CMs outside of their original application, as a supplemental optimization and benchmarking tool to enhance VC technology.

We present a learning-based system for rapid mass-scale material synthesis that is useful for novice and expert users alike. The user preferences are learned via Gaussian Process Regression and can be easily sampled for new recommendations. Typically, each recommendation takes 40-60 seconds to render with global illumination, which makes this process impracticable for real-world workflows. Our neural network eliminates this bottleneck by providing high-quality image predictions in real time, after which it is possible to pick the desired materials from a gallery and assign them to a scene in an intuitive manner. Workflow timings against Disney's "principled" shader reveal that our system scales well with the number of sought materials, thus empowering even novice users to generate hundreds of high-quality material models without any expertise in material modeling. Similarly, expert users experience a significant decrease in the total modeling time when populating a scene with materials. Furthermore, our proposed solution also offers controllable recommendations and a novel latent space variant generation step to enable the real-time fine-tuning of materials without requiring any domain expertise.