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Bots are a lot like humans: Some are cute. Some are ugly. Some are harmless. Some are menacing. Some are friendly. Some are annoying ... and a little racist. Bots serve their creators and society as helpers, spies, educators, servants, lab technicians, and artists. Sometimes, they save lives. Occasionally, they destroy them.

In the 2010s, automation got better, cheaper, and way less avoidable. Its still mysterious, but no longer foreign; the most Extremely Online among us interact with dozens of AIs throughout the day. That means driving directions are more reliable, instant translations are almost good enough, and everyone gets to be an adequate portrait photographer, all powered by artificial intelligence. On the other hand, each of us now sees a personalized version of the world that is curated by an AI to maximize engagement with the platform. And by now, everyone from fruit pickers to hedge fund managers has suffered through headlines about being replaced.

Humans and tech have always coexisted and coevolved, but this decade brought us closer togetherand closer to the futurethan ever. These days, you dont have to be an engineer to participate in AI projects; in fact, you have no choice but to help, as youre constantly offering your digital behavior to train AIs.

So heres how we changed our bots this decade, how they changed us, and where our strange relationship is going as we enter the 2020s.

All those little operational tweaks in our day come courtesy of a specific scientific approach to AI called machine learning, one of the most popular techniques for AI projects this decade. Thats when AI is tasked not only with finding the answers to questions about data sets, but with finding the questions themselves; successful deep learning applications require vast amounts of data and the time and computational power to self-test over and over again.

Deep learning, a subset of machine learning, uses neural networks to extract its own rules and adjust them until it can return the right results; other machine learning techniques might use Bayesian networks, vector maps, or evolutionary algorithms to achieve the same goal.

In January, Technology Reviews Karen Hao released an exhaustive analysis of recent papers in AI that concluded that machine learning was one of the defining features of AI research this decade. Machine learning has enabled near-human and even superhuman abilities in transcribing speech from voice, recognizing emotions from audio or video recordings, as well as forging handwriting or video, Hao wrote. Domestic spying is now a lucrative application for AI technologies, thanks to this powerful new development.

Haos report suggests that the age of deep learning is finally drawing to a close, but the next big thing may have already arrived. Reinforcement learning, like generative adversarial networks (GANs), pits neural nets against one another by having one evaluate the work of the other and distribute rewards and punishments accordinglynot unlike the way dogs and babies learn about the world.

The future of AI could be in structured learning. Just as young humans are thought to learn their first languages by processing data input from fluent caretakers with their internal language grammar, computers can also be taught how to teach themselves a taskespecially if the task is to imitate a human in some capacity.

This decade, artificial intelligence went from being employed chiefly as an academic subject or science fiction trope to an unobtrusive (though occasionally malicious) everyday companion. AIs have been around in some form since the 1500s or the 1980s, depending on your definition. The first search indexing algorithm was AltaVista in 1995, but it wasnt until 2010 that Google quietly introduced personalized search results for all customers and all searches. What was once background chatter from eager engineers has now become an inescapable part of daily life.

One function after another has been turned over to AI jurisdiction, with huge variations in efficacy and consumer response. The prevailing profit model for most of these consumer-facing applications, like social media platforms and map functions, is for users to trade their personal data for minor convenience upgrades, which are achieved through a combination of technical power, data access, and rapid worker disenfranchisement as increasingly complex service jobs are doubled up, automated away, or taken over by AI workers.

The Harvard social scientist Shoshana Zuboff explained the impact of these technologies on the economy with the term surveillance capitalism. This new economic system, she wrote, unilaterally claims human experience as free raw material for translation into behavioural data, in a bid to make profit from informed gambling based on predicted human behavior.

Were already using machine learning to make subjective decisionseven ones that have life-altering consequences. Medical applications are only some of the least controversial uses of artificial intelligence; by the end of the decade, AIs were locating stranded victims of Hurricane Maria, controlling the German power grid, and killing civilians in Pakistan.

The sheer scope of these AI-controlled decision systems is why automation has the potential to transform society on a structural level. In 2012, techno-socialist Zeynep Tufekci pointed out the presence on the Obama reelection campaign of an unprecedented number of data analysts and social scientists, bringing the traditional confluence of marketing and politics into a new age.

Intelligence that relies on data from an unjust world suffers from the principle of garbage in, garbage out, futurist Cory Doctorow observed in a recent blog post. Diverse perspectives on the design team would help, Doctorow wrote, but when it comes to certain technology, there might be no safe way to deploy:

It doesnt help that data collection for image-based AI has so far taken advantage of the most vulnerable populations first. The Facial Recognition Verification Testing Program is the industry standard for testing the accuracy of facial recognition tech; passing the program is imperative for new FR startups seeking funding.

But the datasets of human faces that the program uses are sourced, according to a report from March, from images of U.S. visa applicants, arrested people who have since died, and children exploited by child pornography. The report found that the majority of data subjects were people who had been arrested on suspicion of criminal activity. None of the millions of faces in the programs data sets belonged to people who had consented to this use of their data.

State-level efforts to regulate AI finally emerged this decade, with some success. The European Unions General Data Protection Regulation (GDPR), enforceable from 2018, limits the legal uses of valuable AI training datasets by defining the rights of the data subject (read: us); the GDPR also prohibits the black box model for machine learning applications, requiring both transparency and accountability on how data are stored and used. At the end of the decade, Google showed the class how not to regulate when they built, and then scrapped, an external AI ethics panel a week later, feigning shock at all the negative reception.

Even attempted regulation is a good sign. It means were looking at AI for what it is: not a new life form that competes for resources, but as a formidable weapon. Technological tools are most dangerous in the hands of malicious actors who already hold significant power; you can always hire more programmers. During the long campaign for the 2016 U.S. presidential election, the Putin-backed IRA Twitter botnet campaignsessentially, teams of semi-supervised bot accounts that spread disinformation on purpose and learn from real propagandainfiltrated the very mechanics of American democracy.

Keeping up with AI capacities as they grow will be a massive undertaking. Things could still get much, much worse before they get better; authoritarian governments around the world have a tendency to use technology to further consolidate power and resist regulation.

Tech capabilities have long since proved too fast for traditional human lawmakers, but one hint of what the next decade might hold comes from AIs themselves, who are beginning to be deployed as weapons against the exact type of disinformation other AIs help to create and spread. There now exists, for example, a neural net devoted explicitly to the task of identifying neural net disinformation campaigns on Twitter. The neural nets name is Grover, and its really good at this.

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The Bot Decade: How AI Took Over Our Lives in the 2010s - Popular Mechanics

London, Dec. 10, 2019 (GLOBE NEWSWIRE) -- According to a new market research report Artificial Intelligence in Supply Chain Market by Component (Platforms, Solutions), Technology (Machine Learning, Computer Vision, Natural Language Processing), Application (Warehouse, Fleet, Inventory Management), & End User - Global Forecast to 2027, published by Meticulous Research, the AI in Supply Chain Market is expected to grow at a CAGR of 39.4% from 2019 to reach $21.8 billion by 2027.

Today supply chain networks are becoming more and more complex owing to progressive globalization. Various well-established supply chain organizations across the globe are increasingly struggling with rising cost of operations, dissatisfied customers, declining sales, and unidentified competition. Therefore, the adoption of artificial intelligence technologies in supply chain operations is on the rise in order to create new opportunities & enhance operational capabilities by leveraging new possibilities, fastening processes, and making organizations adaptable to changes in the future.

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Realizing the fact, various end-use industries are investing heavily in order to reap the profits in highly dynamic and competitive market environments. Organizations are aggressively adopting AI-based solutions for supply chain operations to reshape their business processes and increase profitability. Rapid adoption of AI technology across the supply chain operations, rising awareness about artificial intelligence, and widening implementation of computer vision technologies across several end-use industries are the key factors driving steady growth in the global artificial intelligence in supply chain market.

In recent years, the funding for development and implementation of artificial intelligence solutions for supply chain industry has increased significantly. For instance, in 2018, the Government of Qubec invested $60 million in order to support AI-Powered Supply Chains Supercluster (SCALE.AI). Similar investments were also made by the Government of Canada investing up to nearly $230 million for the AI-Powered Supply Chains Supercluster in 2018. Such initiatives are bringing the manufacturing, retail, and information & communications technology sectors on the same platform, to develop intelligent solutions for supply chain management through incorporation of robotics and AI technologies.

The AI in supply chain market study presents historical market data in terms of value (2017 and 2018), estimated current data (2019), and forecasts for 2027 by component, technology, application, end-user, and geography. The study also evaluates industry competitors and analyzes their market share at the global and regional levels.

Based on component, the software segment is estimated to account for the largest share of the overall artificial intelligence in supply chain market in 2019; and is slated to grow at the fastest CAGR during the forecast period. The large share of this segment is attributed to the supply chain visibility offered by software, including inventory control, warehouse management, order procurement, and reverse logistics and tracking.

Based on technology, the machine learning segment is estimated to account for the largest share of the overall AI in supply chain market, in 2019. This is mainly attributed to the growing demand for AI-based intelligent solutions, increasing government initiatives, and ability of AI solutions to efficiently handle and analyze big data and quickly scan, parse, and react to anomalies. On the other hand, computer vision technology is slated to grow at the fastest CAGR during the forecast period, due to widening implementation of computer vision across several end-use industries for monitoring operations, spotting suspicious behavior, and preventing thefts.

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Based on the application, supply chain planning is estimated to hold the largest share of the overall AI market in supply chain, in 2019. This is mainly attributed to the ability of AI solutions to optimize supply chain operations and digitize existing processes and workflows by reinventing the supply chain planning. On the other hand, the demand for AI solutions for warehouse management applications is slated to grow at a fastest CAGR during the forecast period, mainly due to benefits offered by AI solutions in the form of optimizing the logistics, spotting & detecting abnormalities, and automated sorting.

Based on end-user, the consumer-packaged-goods (CPG) segment is estimated to hold the largest share of the overall artificial intelligence in supply chain market in 2019, due to expanding e-commerce sector and ability of AI solutions to provide profitable drop-shipping with features like product tracking, inventory management, and warehouse management. On the other hand, the retail segment is slated to grow at the fastest CAGR during the forecast period, mainly due to benefits of AI in the form of addressing issues with stocking inefficiencies, complexity of operations, and high product lead times in supply chain operations of the retail industry.

The report also includes an extensive assessment of the key strategic developments adopted by leading market participants in the AI in supply chain industry over the past 4 years (2016-2019). The artificial intelligence in supply chain market has witnessed number of partnerships & agreements in the recent years. For instance, in December 2018, Google announced a strategic partnership with Iguazio to provide real-time supply chain and inventory management services for the retail sector.

The global artificial intelligence in supply chain market is highly fragmented with the presence of key players, such asIntel Corporation (U.S.), Amazon.com, Inc. (U.S.), Google LLC (U.S.), Microsoft Corporation (U.S.), Nvidia Corporation (U.S.), Oracle Corporation (U.S.), IBM Corporation (U.S.), Samsung (South Korea), LLamasoft Inc. (U.S.), SAP (Germany), General Electric (U.S.), Deutsche Post AG DHL (Germany), Xilinx (U.S.), Micron Technology, Inc. (U.S.), FedEx (U.S.), and ClearMetal, Inc. (U.S.) along with several local and regional players.

Browse key industry insights spread across 180 pages with 167 market data tables & 29 figures & charts from the market research report:https://www.meticulousresearch.com/product/artificial-intelligence-ai-in-supply-chain-market-5064/

Scope of the Report:

AI in Supply Chain Market, by Component

AI in Supply Chain Market, by Technology

AI in Supply Chain Market, by Application

AI in Supply Chain Market, by End User

AI in Supply Chain Market, by Geography

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Related Reports:Artificial Intelligence in Retail Market by Product (Solution and Services), Application (Predictive Merchandizing, Programmatic Advertising, Market Forecasting, In-store Visual Monitoring and Surveillance, Location-based Marketing), Technology (Machine Learning, Natural Language Processing), Deployment (Cloud, On-premises) and Geography - Global Forecasts to 2025, read report:https://www.meticulousresearch.com/product/artificial-intelligence-in-retail-market-4979/

Artificial Intelligence in Healthcare Market by Product (Hardware, Software, Services), Technology (Machine Learning, Context-Aware Computing, NLP), Application (Drug Discovery, Precision Medicine), End User, And Geography - Global Forecast to 2025, read report:https://www.meticulousresearch.com/product/artificial-intelligence-healthcare-market/

About Meticulous Research

The name of our company defines our services, strengths, and values. Since the inception, we have only thrived to research, analyze and present the critical market data with great attention to details.

Meticulous Research was founded in 2010 and incorporated as Meticulous Market Research Pvt. Ltd. in 2013 as a private limited company under the Companies Act, 1956. Since its incorporation, with the help of its unique research methodologies, the company has become the leading provider of premium market intelligence in North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa regions.

With the meticulous primary and secondary research techniques, we have built strong capabilities in data collection, interpretation, and analysis of data including qualitative and quantitative research with the finest team of analysts. We design our meticulously analyzed intelligent and value-driven syndicate market research reports, custom studies, quick turnaround research, and consulting solutions to address business challenges of sustainable growth.

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Artificial Intelligence (AI) in Supply Chain Market Worth $21.8 billion by 2027- Exclusive Report by Meticulous Research - GlobeNewswire

Baidu, Inc. has filed the most AI-related patent applications in China, a recognition of the companys long-term commitment to driving technological advancement, a recent study from the research unit of Chinas Ministry of Industry and Information Technology (MIIT) has shown.

Baidu filed a total of 5,712 AI-related patent applications as of October 2019, ranking No.1 in China for the second consecutive year. Baidus patent applications were followed by Tencent (4,115), Microsoft (3,978), Inspur (3,755), and Huawei (3,656), according to the report issued by the China Industrial Control Systems Cyber Emergency Response Team, a research unit under the MIIT.

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Baidu retained the top spot for AI patent applications in China because of our continuous research and investment in developing AI, as well as our strategic focus on patents, said Victor Liang, Vice President and General Counsel of Baidu.

In the future, we will continue to increase our investments into securing AI patents, especially for high-value and high-quality patents, to provide a solid foundation for Baidus AI business and for our development of world-leading technology, he said.

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The report showed that Baidu is the patent application leader in several key areas of AI. These include deep learning (1,429), natural language processing (938), and speech recognition (933). Baidu also leads in the highly competitive area of intelligent driving, with 1,237 patent applications, a figure that surpasses leading Chinese universities and research institutions, as well as many international automotive companies. With the launch of the Apollo open source autonomous driving platform and other intelligent driving innovations, Baidu has been committed to pioneering the intelligent transformation of the mobility industry.

After years of research, Baidu has developed a comprehensive AI ecosystem and is now at the forefront of the global AI industry. Moving forward, Baidu will continue to conduct research in the core areas of AI, contribute to scientific and technological innovation in China, and actively push forward the application of AI into more vertical industries. Baidu is positioned to be a global leader in a wave of innovation that will transform industries.

Marketing Technology News: Top 5 Technology Trends to Impact the Digital Infrastructure Landscape in 2020

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Baidu Leads the Way in Innovation with 5,712 Artificial Intelligence Patent Applications - MarTech Series

Ms Rich pointed to online searches for jobs where the term 'CEO' displays 11 per cent women and said while this may be accurate in terms of the number of women CEOs it means algorithms are only surfacing executive job positions to men.

"The algorithms are learning off data that is saying essentially women can't be CEOs, that is where it becomes concerning," she said.

Ms Rich said facial recognition software is 99 per cent accurate for white men but accuracy drops significantly for women and people from other backgrounds because the largest facial data set in the world is based on 80 per cent men and 75 per cent white men.

"For me, I am not a white man, I still need to work in the world and have facial recognition work for me," she said. "As soon as you are a woman of colour you are completely shut out of this."

Skewed data sets were highlighted by Caroline Criado Perez in her book Invisible women: Exposing data bias in a world designed for men which outlines a statistical "silence" about half of humanity.

It's a problem the government is looking to address with its AI Ethics Framework which includes the principle of fairness.

Last month the National Australia Bank, Commonwealth Bank, Telstra, Microsoft and Flamingo AI signed up to test the principles which state: "Throughout their lifecycle, AI systems should be inclusive and accessible, and should not involve or result in unfair discrimination against individuals, communities or groups."

Ms Rich said Microsoft was working to create more diverse facial recognition data sets by overlaying existing data sets with new data.

She called on startups and product developers to instil a more diverse way of thinking in order to avoid bias in AI.

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This could be done by hiring diverse teams, using and creating high-quality data sets, checking bias and auditing algorithms.

"It's really, really, really crucial that you're auditing any algorithm that you're creating to makesure that it's actually working for everyone," she said. "Because we're building products for everyone."

Follow MySmallBusiness on Twitter, Facebook and LinkedIn.

Cara is the small business editor for The Age and The Sydney Morning Herald based in Melbourne

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Microsoft tech expert warns of bias and sexism in artificial intelligence - The Age

SINGAPORE, Dec. 10, 2019 /PRNewswire/ -- The Chinese Association for Artificial Intelligence (CAAI) is the only state-level Science and Technology organization in the field of Artificial Intelligence under the Ministry of Civil Affairs in the People's Republic of China. The 6th IEEE International Conference on Cloud Computing and Intelligence Systems is held from 19 December to 21 December 2019 at Carlton Hotel Singapore and is hosted by the CAAI, IEEE Beijing Section, co-organised by Tsinghua University, National University of Singapore, Shenzhen University of China,Pensees Pte Ltd and AI Pharos Pte Ltd.

Experts and scholars from various places will jointly host a feast of academic exchanges. At this conference, Professor Fuchun Sun from the Department of Computer Science and Technology of Tsinghua University, Dr. Wang Bin, Director of Xiaomi's AI Lab, and Provost's Chair Professor Ng Hwee Tou of the National University of Singapore are special guest speakers to discuss and share the latest academic trend.

CCIS 2019- Industry Track

For the first day of the conference on 19 December 2019, an Industry Track program with the theme, "Driving Industry Transformation with Intelligent Systems", is now open for public registration. In order to expand international exchanges and cooperation in the field of cloud computing and intelligent science and technology, and to enhance academic influence in this field, the Industry Track program focuses on in-depth research and discussion on cutting-edge technologies and hot issues in the fields of cloud computing, big data, computer vision, and artificial intelligence, thus to promote the development of related technologies and industries.

Industry Track Co-Chairs and Keynote speakers include:

CCIS 2019 - Industry Track is co-organised by AI Pharos Pte Ltd, a Deep Tech Community entity in Singapore. Tickets for CCIS 2019 - Industry Track is now available on https://www.eventbrite.com/e/ccis-2019-conference-industry-track-tickets-82770403505

CCIS 2019 Details:

Time: December 19, 2019Location: CARLTON Hotel, Bugis, Singapore

SOURCE AI Pharos Pte Ltd

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Chinese Association of Artificial Intelligence is hosting the 6th IEEE International Conference on the AI Pharos Pte Ltd co-organised Cloud Computing...