In late September, Beijing unveiled to the world Daxing, a glimmering $11 billion airport showcasing technologies such as robots and facial recognition scanners that many other airports worldwide are either adopting or are now considering. Daxing fits the description of what experts hail as a "smart airport." Just as a smart home is where internet-connected devices control functions like security and thermostats, smart airports use cloud-based technologies to simplify and improve services. Of course, many of the nearly 4,000 scheduled service airports across the world are still embarrassingly antiquated. The good news for aviation is that more facilities are investing, finally, to better serve airlines, suppliers, and travelers. This year, airports worldwide will spend $11.8 billion -- 68 percent more than the level three years ago -- on information technology, according to an estimate published this month by SITA (Société Internationale de Telecommunications Aeronautiques, an airline-owned tech provider). A few trends are driving the rise of smart airports. Flight volumes are increasing, so airports need better ways to process flyers. Airports need better ways to make money, too, by encouraging passengers to spend more in their shops and restaurants. Data is growing in importance. Everything happening at an airport, from where passengers are flowing to which items are selling in stores, generates data. Airports can analyze this data to spot opportunities for eking out fatter profits. They can sell the data to third-parties as well.

The battle for AI supremacy: Yet after years of threatening U.S. leadership in artificial intelligence, China is still lagging behind the U.S. in investment, innovation, and implementation of artificial intelligence (AI). Although America still ranks number one, a new study has placed China second in the world for the development and implementation of AI. China was followed by the United Kingdom, Canada, and Germany in the ranking. The rankings had France in sixth place, followed by Singapore, South Korea, Japan, and Ireland. European countries were prominent on the list, of the top-twenty nations, 11 were in Europe, 6 in Asia, two in North America, and one in Oceania.

The guide traces the significance of activity for Australia to catch the advantages of AI, which is evaluated to be worth AU$ 22.17 trillion to the worldwide economy by 2030. The guide is proposed to help control future interest in AI and AI and goes with Artificial Intelligence: Australia's Ethics Framework, an exchange paper arranged by CSIRO's Data61 and distributed by the Australian Government in April 2019. Dr. Stefan Hajkowicz, the senior research researcher at CSIRO's Data61 and lead creator of the guide, disclosed that the way into Australia's AI-empowered future is through mechanical specialization.CSIRO's Chief Executive, Dr. Larry Marshall, shared how AI quickened the pace and size of settling the best difficulties through inventive science and innovation. Man-made intelligence speaks to a noteworthy chance to convey social, natural, and financial advantages. It can help profitability through its solid potential to empower the industry to improve items, convey better administrations, quicker, less expensive, and more secure. It uses the skill of CSIRO's information science and computerized arm, Data61.

The challenge most CTOs face is that automation -- up until this point -- has been a largely organic affair. Different organisational groups handle it in separate, often duplicative, ways. That has led to "islands of automation" in many organisations: redundant processes and tools duplicated with little regard for standards, governance, or metrics relevant to the business such as product revenue or time to market. In 2020, CTOs will see automation challenges arise from this. In 2020, 3.9% of cubicle jobs will be removed from the economy.

The following keynote speakers have been confirmed for IEEE GLOBECOM 2019. Abstract: We are well into the "Internet of Things" era for the Internet. Billions of devices are expected and it is not uncommon to find a dozen or even a score of Internet-enabled devices in residences and offices around the world. These systems run on software - some of which has not been well tested for safety and security. We need to introduce and promote an ethic of software safety and extended maintenance to protect the users of these devices.

Take a look around and you will find a lot of self-proclaimed marketing professionals. However, there are only a few names who have actually made it to the top and gotten their art acknowledged. Today on Branex Talks, we are privileged to have such a gentleman with us. To date, he has helped businesses and startups founders scale their business. He has extensive experience working with various professionals in 70 countries, including Taiwan, Spain, Ireland, the US and the UK.

The context: The vast majority of Facebook's moderation is now done automatically by the company's machine-learning systems, reducing the amount of harrowing content its moderators have to review. In its latest community standards enforcement report, published earlier this month, the company claimed that 98% of terrorist videos and photos are removed before anyone has the chance to see them, let alone report them. So, what are we seeing here? The company has been training its machine-learning systems to identify and label objects in videos--from the mundane, such as vases or people--to the dangerous, such as guns or knives. Facebook's AI uses two main approaches to look for dangerous content. One is to employ neural networks that look for features and behaviors of known objects and label them with varying percentages of confidence (as we can see in the video above).

No one knows who gave Rahul Roy tuberculosis. Roy's charmed life as a successful trader involved traveling in his Mercedes C class between his apartment on the plush Nepean Sea Road in South Mumbai and offices in Bombay Stock Exchange. He cared little for Mumbai's weather. He seldom rolled down his car windows – his ambient atmosphere, optimized for his comfort, rarely changed. Historically TB, or "consumption" as it was known, was a Bohemian malady; the chronic suffering produced a rhapsody which produced fine art. TB was fashionable in Victorian Britain, in part, because consumption, like aristocracy, was thought to be hereditary. Even after Robert Koch discovered that the cause of TB was a rod-shaped bacterium – Mycobacterium Tuberculosis (MTB), TB had a special status denied to its immoral peer, Syphilis, and unaesthetic cousin, leprosy. TB became egalitarian in the early twentieth century but retained an aristocratic noblesse oblige. George Orwell may have contracted TB when he voluntarily lived with miners in crowded squalor to understand poverty. Unlike Orwell, Roy had no pretentions of solidarity with poor people. For Roy, there was nothing heroic about getting TB. He was embarrassed not because of TB's infectivity; TB sanitariums are a thing of the past. TB signaled social class decline. He believed rickshawallahs, not traders, got TB.

If recent television shows are anything to go by, we're a little concerned about the consequences of technological development. Black Mirror projects the negative consequences of social media, while artificial intelligence turns rogue in The 100 and Better Than Us. The potential extinction of the human race is up for grabs in Travellers, and Altered Carbon frets over the separation of human consciousness from the body. And Humans and Westworld see trouble ahead for human-android relations. Narratives like these have a long lineage.

A wave of recent scholarship documenting the discriminatory harms of algorithmic systems has spurred widespread interest in algorithmic accountability and regulation. Yet effective accountability and regulation is stymied by a persistent lack of resources supporting public understanding of algorithms and artificial intelligence. Through interactions with a US-based civil rights organization and their coalition of community organizations, we identify a need for (i) heuristics that aid stakeholders in distinguishing between types of analytic and information systems in lay language, and (ii) risk assessment tools for such systems that begin by making algorithms more legible. The present work delivers a toolkit to achieve these aims. This paper both presents the Algorithmic Equity Toolkit (AEKit) Equity as an artifact, and details how our participatory process shaped its design. Our work fits within human-computer interaction scholarship as a demonstration of the value of HCI methods and approaches to problems in the area of algorithmic transparency and accountability.