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Category Archives: Artificial Intelligence
Posted: November 19, 2019 at 11:44 am
From the sixth (and most recent) installment of NYU ITPs academic journal Adjacent, entitled Old/New/Next, senior editor Gabriella Garcias essay There Is No Artificial Intelligence: A Conversation with the Initiative for Indigenous Futures seeks answers to two questions: what makes something artificial and how do we determine intelligence? Garcia references the MUTEK Montreal electronic arts festival and a symposium by members of Initiative for Indigenous Futures. IIF co-founder Professor Jason Edward Lewis and Lakota performance artist Suzanne Kite address everything from machine learning, programmed emotions, and the implementation of white supremacy in AI. Observation thus far has been that biases are entrenched in the algorithms coded into our technologyand now is the time to make change. Read more at Adjacent.
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Artificial Intelligence (AI) in Retail Market worth $15.3 billion by 2025 – Exclusive Report by Meticulous Research – GlobeNewswire
Posted: at 11:44 am
London, Nov. 19, 2019 (GLOBE NEWSWIRE) -- According to a new market research report Artificial Intelligence (AI) 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, published by Meticulous Research, the global AI in retail market is expected to grow at a CAGR of 35.9% from 2019 to reach $15.3 billion by 2025.
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Over the past few years, digital technologies are being embedded into core value-generation processes in society and businesses by creating innovation. The growing number of millennials with their inclination towards digital-first approaches is putting organizations under constant pressure to innovate; thus, making artificial intelligence (AI) a top priority for retail businesses. Various well-established retailers are struggling with increasing cost, dissatisfied customers, declining sales and upstart competition. Implementing artificial intelligence in retail creates new opportunities and capabilities for retailers by leveraging new possibilities, fastening processes, and making organizations adaptable to changes in the future. Realizing the fact, retail companies are investing in billions to reap benefits of AI technology and improve profitability of their businesses. Strong participation of industry players in leveraging AI technology is reshaping the technology landscape of the retail industry.
The overall artificial intelligence in the retail market is witnessing a consistent penetration of smartphones & connected devices, advancements in big data for retail sector, rapid adoption of advancement in technology across the retail chain, and increasing adoption of the multi-channel or omnichannel retailing strategy. Furthermore, the efforts from retailers to gain access to more customers, enhance business visibility, and build customer loyalty are also playing a vital role in driving adoption of AI technology in the retail industry. The increasing adoption of AI-powered voice enabled devices owing to their benefits in the form of enhanced user experience and improved productivity are also contributing to the market growth.
The global artificial intelligence market in retail is majorly segmented by product offering, application, learning technology, type, deployment type, and geography. Based on product offering, the global AI in retail market is majorly segmented into solutions and services. The solution segment is categorized into chatbot, customer behavior tracking, customer relationship management (CRM), inventory management, price optimization, recommendation engines, supply chain management, and visual search. The service segment is further segmented into managed services and professional services. Recommendation engine dominates the AI solutions market for the retail industry and it is expected to register a strong growth over the forecast period. The features in terms of enhanced user experiences, better customer engagement, precise recommendations of products, and personalized recommendation is helping recommendation engines to maintain their growth in the global artificial intelligence in retail market.
Based on application, the overall AI in retail market is majorly segmented into predictive merchandising, programmatic advertising, market forecasting, in-store visual monitoring & surveillance, and location-based marketing. In-store visual monitoring and surveillance applications are spearheading the growth of the AI market in the retail industry. This segment is expected to register a steady growth over the coming years and continue its dominance during the forecast period. Its benefits in the form of better inventory tracking, customer traffic monitoring, enhanced safety protocols, prevention against shoplifting, outpacing shrink caused by employee theft, vendor fraud, and administrative errors are contributing to the growth of this segment.
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Geographically, the global artificial intelligence in retail market is segmented into five major regions, namely, North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. The global AI in retail market is analyzed methodically with respect to major countries in each of the regions with the help of bottom-up approach to arrive at the most precise market estimation. At present, North America holds a dominating position in the global AI in retail market. The region has high technology adoption rate, presence of key players & start-ups, and high penetration of internet. Consequently, North America is expected to retain its dominance throughout the forecast period. However, factors such as rapid growth in consumer spending, presence of young population, government initiatives towards digitization, developing internet and connectivity infrastructure, and growing adoption of AI-based solutions and services among retailers are helping Asia Pacific region to register the fastest growth in the global artificial intelligence in retail market.
The global artificial intelligence (AI) in retail market is consolidated and dominated by few major players namely, Amazon.com, Inc. (U.S.), Google LLC (U.S.), IBM Corporation (U.S.), Intel Corporation (U.S.), Microsoft Corporation (U.S.), Nvidia Corporation (U.S.), Oracle Corporation (U.S.), SAP SE (Germany), Salesforce.com, Inc. (U.S.), and BloomReach, Inc. (U.S.) along with several local and regional players.
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Automotive Artificial Intelligence (AI) Market by Offering (Hardware, Software), Technology (Machine Learning, Deep Learning, Computer Vision, Context Awareness, Natural Language Processing), Process (Signal Recognition, Image Recognition, Voice Recognition, Data Mining), Drive (Autonomous Drive, Semi-autonomous Drive), and Region Global Forecast to 2025, read more:https://www.meticulousresearch.com/product/automotive-artificial-intelligence-market-4996/
Artificial Intelligence (AI) in Manufacturing Market by Offering (Hardware, Software, and Services), End-use Industry (Semiconductors and Electronics, Energy and power, Pharmaceuticals, Chemical, Medical Devices, Automobile, Heavy Metal and Machine Manufacturing, Food and Beverages, Others), Technology (Machine Learning, NLP, Context-Aware Computing, and Computer Vision), Application (Predictive Maintenance, Material Movement, Production Planning, Field Services, Quality Management, Cybersecurity, Industrial Robotics, and Reclamation), and Region - Global Forecast to 2025, read more:https://www.meticulousresearch.com/product/artificial-intelligence-in-manufacturing-market-4983/
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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.
Posted: at 11:44 am
LITTLETON, Colo. -- A group of MIT researchers visited Lockheed Martin this month for a chance to talk about the future of artificial intelligence and automation.
Liz Reynolds is the executive director of the MIT Task Force on the Work of the Future and says her job is to focus on the relationship between new technologies and how they will affect jobs.
Colorado is at the forefront of thinking about these things, Reynolds said. All jobs will be affected by this technology.
Earlier this year, U.S. Sen. Michael Bennet, D-Colo., created an artificial intelligence strategy group to take a closer look at how AI is being used in the state and how that will change in the future.
We need a national strategy on AI that galvanizes innovation, plans for the changes to our workforce, and is clear-eyed about the challenges ahead. And while were seeing progress, workers and employers cant wait on Washington, said Sen. Bennet in a statement. Colorado is well-positioned to shape those efforts, which is why weve made it a priority to bring together Colorado leaders in education, business, nonprofits, labor, and government to think through how we can best support and train workers across Colorado so they are better prepared for a changing economy."
MIT recently released a 60-page report detailing some of the possibilities and challenges with AI and automation.
One of the major challenges the group is considering is how the technology will affect vulnerable workers, particularly people who do not have a four-year degree.
The MIT team is looking for ways to train those workers to better prepare them for the changes.
Were not trying to replace a human, thats not something youre ever going to do with eldercare. For example, youre going be looking for ways to use this technology to help, Reynolds said.
Despite recent advances in AI, Reynolds believes the changes to the workforce will happen over a matter of decades, not years.
We think its going to be a slower process and its going to give us time to make the changes that we need institutionally, she said.
Beyond that, projections suggest that, with an aging workforce, there will be a scarcity of people to employ in future and technology can help fill some of those gaps.
The bigger question is how to ensure that workers can get a quality job that results in economic security for their families.
I think theres really an opportunity for us to see technology not as a threat but really, as a tool, Reynold said. If we can use the right policies and institutions to support workers in this transition then we could really be working toward something that works for everyone.
Lockheed Martin has been using artificial intelligence and automation in its space program for years. The companys scientists rely on automation to manage and operate spacecrafts that are on missions.
However, the technology is also being applied closer to home. The AI Lockheed Martin has created is already being applied to peoples day-to-day lives, from GPS navigation to banking. Now, the company is looking for more ways to make use of it.
Even though its been around for some time, we want to think about how we can use it in different, emerging ways and apply it to other parts of our business as well, said Whitley Poyser, the business transformation acting director for Lockheed Martin Space.
One of the areas in particular Lockheed Martin is looking to apply the technology is in its manufacturing, not only to streamline processes but to use data the machines are already collecting to predict potential issues and better prepare for them.
Poyser understands that there are some fears about this technology taking over jobs, but she doesnt believe thats the case.
Its not taking the job away, its just allowing our employees to think differently and think about elevating their skills and their current jobs, Poyser said. Its actually less of a fear to us and more of an opportunity.
The true potential of artificial intelligence is only beginning to be unleashed for companies like Lockheed Martin. Reynolds is hoping that predicting for the possibilities and challenges now will help the country better prepare for the changes in the decades to come.
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Posted: at 11:44 am
By Jeff Mills, Director, Solution Marketing at SAP SuccessFactors
Its no longer a secret that getting past the robot rsum readers to a human let alone land an interview can seem like trying to get in to see the Wizard of Oz. As the rsums of highly qualified applicants are rejected by the initial automated screening, job seekers suddenly find themselves having to learn rsum submission optimization to please the algorithms and beat the bots for a meeting with the Wizard.
Many enterprise businesses use Artificial Intelligence (AI) and machine learning tools to screen rsums when recruiting and hiring new employees.Even small to midsize companies who use recruiting services are using whatever algorithm or search-driven automated rsum screening those services utilize.
Why dont human beings read rsums anymore? Well, they do, but usually later in the process after the initial shortlist by the bots. Unfortunately, desirable soft skills and unquantifiable experience can go unnoticed by the best-trained algorithms. So far, the only solution is human interaction.
Despite the view from outside the organization, HR has good reason for using automated processes for screening rsums. To efficiently manage the hundreds or even thousands of applications submitted for one position alone, companies have adopted automated AI screening tools to not only save time and human effort but also to find qualified and desirable candidates before they move on or someone else gets to them first.
Nobodys ever seen the Great Oz!
The wealth of impressive time-saving and turnover reduction metrics equates to success and big ROI for organizations who automate recruiting and hiring processes. Most tales of headaches and frustration go untold for many thousands of qualified applicants whose rsums somehow failed to tickle the algorithm just right.
This trend is changing, however, as the bias built into AI and machine learning algorithms unintentionally or otherwise becomes more glaringly apparent and undeniable. Sure, any new technology will have its early adopters and zealous promoters and apologists as well as the naysayers and skeptics. But when that technology shows promise to change industry and increase profit, criticism can be drowned out and ignored.
The problem of bias in AI is not a new concern. For several years, scientists and engineers have warned that because AI is created and developed by humans, the likelihood of bias finding its way into the program code is high if not certain. And the time to think about that and address it as much as possible is during the design, development, and testing process. Blind spots are inevitable. Once buy-in is achieved and business ecosystems integrate that technology, the recursive and reciprocal influences of technology, commerce, and society can make changing course slow and/or costly.
Consider the recent trouble Amazon found itself in for some of its hiring practices when it had been determined that their AI recruiting tool was biased against women. AI in itself is not biased and performs only as it is instructed and adapts to new information. Rather, the bias comes from the way human beings program and develop the way machines learn and execute commands. Or if the outputs of the AI are taken at face value and never trained by ongoing human interaction, they can never adapt.
Bias enters in a few ways. One source is rooted in the data sets used to train algorithms for screening candidates. Other sources of bias enter when certain criteria are privileged, such as growing up in a certain area, attending a top university, or certain age preferences. By using the data for existing employees as a model for qualified candidates, the screening process can become a kind of feedback loop of biased criteria.
A few methods and practices can help correct or avoid this problem. One is to use broad swaths of data, including data from outside your company and even your industry. Also, train algorithms on a continual basis, incorporating new data, and monitoring algorithm function and results. Set benchmarks for measuring data quality and have humans screen rsums as well. Management of automated recruiting and screening solutions can go a long way in minimizing bias as well as reducing the number of qualified candidates who get their rsums rejected.
Bell out of order, please knock
As mentioned earlier, change takes time once these processes are in place and embedded. Until widespread acceptance that problems exist, and steps are taken to address them, the best job seekers can do is adapt.
With all of the possible ways that programmers biases influence the bots screening rsums, what can people applying for jobs do to improve their chances of getting past the AI gatekeepers?
The good news is that these moves will not only help eliminate false negatives and keep your rsum out of the abyss, but they are likely to make things easier for the human beings it reaches.
Well, why didnt you say so? Thats a horse of a different color!
So, what are they looking for? How do you beat the bots?
In the big picture, AI is still young, and we are working out the kinks and bugs not only at a basic code and function level, but also on the human level. We are still learning how to navigate and account for our roles and responsibilities in the overall ecosystem of human-computer interaction.
The bottom line is that AI, machine learning, and automation can eliminate bias or reinforce it. That separation may never be pure, but its an ideal that is not only worth striving for, it is absolutely necessary to work toward. The impact and consequences of our choices today will leave long-lasting effects on every area of human life.
And the bright side is that were already beginning to see how those theoretical concerns can play out in the real world, and we have an opportunity to improve a life-changing technological development whose reach and impact we can still only dimly imagine. In the meantime, job seekers looking to beat the bots are not entirely powerless, but can do what human beings have done well for ages: adapt.
Interested in how to deliver a great candidate experience? Read our guide on how to Transform the Candidate Experience.
Public fears about artificial intelligence are ‘not the fault of A.I.’ itself, tech exec says – CNBC
Posted: at 11:44 am
Rong Luo, CFO of TAL Education Group, Doranda Doo, SVP of iFLYTEK Co. Ltd. and Song Zhang, Managing Director of Thoughtworks China on Day 2 of CNBC East Tech West at LN Garden Hotel Nansha Guangzhou on November 19, 2019 in Nansha, Guangzhou, China.
Zhong Zhi | Getty Images News | Getty Images
The technology industry and policymakers need to address public concerns about artificial intelligence (AI) which are "not the fault of AI" itself, a tech executive said Tuesday.
"It is the fault of developers, so we need to solve this problem," said Song Zhang, managing director for China at global software consultancy, ThoughtWorks.
Consumer worries relating to AI include concerns about personal privacy and how the systems may get out of control, said Zhang during a panel discussion discussing the "Future of AI" at CNBC's East Tech West conference in the Nansha district of Guangzhou, China.
It is the duty of the tech industry and policymakers to focus on, discuss and solve such problems, said Zhang in Mandarin, according to a CNBC translation. Indeed, while consumers are curious about AI when they first come into contact with the technology, their mindset changes over time, said Rong Luo, chief financial officer of TAL Education Group.
"The first phase is everyone finds it refreshing, they like something new, they want to give it a try," said Luo.
But "in phase two, people start to care a lot about their privacy, their security," Luo added.
And finally, after "one to two years of adjustments, we (have) now entered phase three, we have a more objective view of the technology. We do not put (it) on the pedestal nor do we demonize it," said Luo.
Panelists at the session acknowledged the potential of AI in various fields such as language translation and education.
"Technology is here to assist them, empower them. We want to free them from those repetitive and meaningless work (tasks) so they have more energy and time for other more creative jobs," said Doranda Doo, senior vice president of Chinese artificial intelligence firm iFlytek.
"So I think what's the most powerful is not AI itself, but people who are empowered by AI," Doo said.
Posted: at 11:44 am
For many, just mentioning artificial intelligence brings up mental images of sentient robots at war with mankind and mans struggle to avoid the endangered species list. While this may one day be a real scenario for when (perhaps a big if?) mankind ever creates an artificial general intelligence (AGI), the more pressing matter is whether embedded software developers should be embracing or fearing the use of artificial intelligence in their systems. Here are five reasons why you may want to include machine learning in your next project.
Reason #1 Marketing Buzz
From an engineering perspective, including a technology or methodology in a design simply because it has marketing buzz is something that every engineer should fight. The fact though is that if there is a buzz around something, odds are it will in the end help to sell the product better. Technology marketing seems to come in cycles, but there are always underlying themes that are driving those cycles that at the end of the day do turn out to be real.
Artificial intelligence has progressed through the years, with deep learning on the way. (Image source: Oracle)
Machine learning has a ton of buzz around it right now. Im finding this year that had industry events, machine learning typically makes up at least 25% of the event talks. Ive had several clients tell me that they need machine learning in their product and when I ask them their use case and why they need it, the answer is just that they need it. Ive heard this same story from dozens of colleagues, but the push for machine learning seems relentless right now. The driver is not necessarily engineering, but simply leveraging industry buzz to sell product.
Reason #2 The Hardware Can Support It
Its truly amazing how much microcontroller and application processors have changed in just the last few years. Microcontrollers which I have always considered to be resource constrained devices are now supporting megabytes of flash and RAM, having on-board cache and reaching system clock rates of 1 GHz and beyond! These little controllers are now even supporting DSP instructions which means that they can efficiently execute inferences.
With the amount of computing power available on these processors, it may not require much additional cost on the BOM to be able to support machine learning. If theres no added cost, and the marketing department is pushing for it, then leveraging machine learning might make sense simply because the hardware can support it!
Reason #3 It May Simplify Development
Machine learning has risen on the buzz charts for a reason. It has become a nearly indispensable tool for the IoT and the cloud. Machine learning can dramatically simplify software development. For example, have you ever tried to code up an application that can recognize gestures, handwriting or classify objects? These are really simple problems for a human brain to solve, but extremely difficult to write a program for. In certain program domains such as voice recognition, image classification and predictive maintenance, machine learning can dramatically simplify the development process and speed-up development.
With an ever expanding IoT and more data than one could ever hope for, its becoming far easier to classify large datasets and then train a model to use that information to generate the desired outcome for the system. In the past, developers may have had configuration values or acceptable operation bars that were constantly checked during runtime. These often involved lots of testing and a fair amount guessing. Through machine learning this can all be avoided by providing the data, developing a model and then deploying the inference on an embedded systems.
Reason #4 To Expand Your Solution Toolbox
One aspect of engineering that I absolutely love is that the tools and technologies that we use to solve problems and development products is always changing. Just look at how you developed an embedded one, three and five years ago! While some of your approaches have undoubtedly stayed constant, there should have been considerable improvements and additions to your processes that have improved your efficiency and the way that you solve problems.
Leveraging machine learning is yet another tool to add to the toolbox that in time, will prove to be an indispensable tool for developing embedded systems. However, that tool will never be sharpened if developers dont start to learn about, evaluate and use that tool. While it may not make sense to deploy a machine learning solution for a product today or even next year, understanding how it applies to your product and customers, the advantages and disadvantages can help to ensure that when the technology is more mature, that it will be easier to leverage for product development.
Real Value Will Follow the Marketing Buzz
There are a lot of reasons to start using machine learning in your next design cycle. While I believe marketing buzz is one of the biggest driving forces for tinyML right now, I also believe that real applications are not far behind and that developers need to start experimenting today if they are going to be successful tomorrow. While machine learning for embedded holds great promise, there are several issues that I think should strike a little bit of fear into the cautious developer such as:
These are concerns for a later time though, once weve mastered just getting our new tool to work the way that we expect it to.
Jacob Beningo is an embedded software consultant who currently works with clients in more than a dozen countries to dramatically transform their businesses by improving product quality, cost and time to market. He has published more than 200 articles on embedded software development techniques, is a sought-after speaker and technical trainer, and holds three degrees which include a Masters of Engineering from the University of Michigan. Feel free to contact him at [emailprotected], at his website, and sign-up for his monthly Embedded Bytes Newsletter.
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Posted: at 11:44 am
Artificial intelligence (AI) is more than just cyborgs in movies trying to destroy humanity. It has actual real-world applications that can make our lives better and our businesses stronger and more profitable. In retail, artificial intelligence is being adopted rapidly - between 2016 and 2018 there was a 600% increase in adoption. Unfortunately the adoption rate is still relatively low, ranging from 26% for home improvement stores to 33% for apparel and footwear. If AI can make such a big difference, why isnt everyone adopting it?
Currently, only 15% of companies are saying they are spearheading AI adoption. Only 25% of large retailers are investing up to 10% of their capital in artificial intelligence systems, while all other size retailers are spending just 7%. Implementation of AI is costly, but those who use it are finding the benefits far outweigh the costs.
Customer service is one of the most successful use-cases for artificial intelligence yet. Customer-facing AI can improve customer satisfaction by 9%, reduce customer complaints by 8%, and can lower customer churn by 5%, all in the early stages. As this technology improves, it can help even more with customer satisfaction. Currently, voice technology is being programmed that can emulate and mirror human emotions better, mimicking empathy and deescalating tense customer service situations. But even just getting people to the right customer service representative when they call a call center can help get customers the right remedy to their situation.
Chatbots and virtual assistants are an extension of this technology that are used to assist customers who arent having a problem. They can help assist customers in finding the right item to buy, finding new items to try, and learning more about what they are buying. In fashion, AI and chatbots are being used to help customers find new clothing looks based on what they like. In the spirits business, Brown-Forman has a Whiskey Whisperer that can help customers learn more about whiskey, find new products, and find cocktail recipes to try.
AI is also helping to streamline the shopping experience. In Amazon Go stores, cameras and AI mean that customers can take items off the shelves, bag them and walk out of the store, saving time in the checkout line, as the system rings things up as you go and automatically charges you as you walk out the door.
Throughout the supply chain, AI can be used to streamline operations. In production, AI can be used to forecast orders, schedule workers optimally, and even create a schedule that will utilize electricity more efficiently. In shipping, AI can help ensure trucks are completely full so empty space isnt being shipped. It can also optimize shipping routes to save on time and fuel. In retail, AI can be used to optimize ordering so that valuable inventory isnt sitting around collecting dust.
Artificial intelligence is often portrayed as something that is going to destroy mankind in the movies, but in reality most of the real-life applications are pretty mundane and most are actually beneficial. As AI is adopted in more aspects of the retail landscape, there will certainly be bumps in the road. But the end product will be a stronger, more agile sector of the economy that serves both customers and businesses better. Learn more about the future of retail with AI from the infographic below!
Read next: From Science Fiction To Reality With Artificial intelligence (infographic)
Posted: at 11:44 am
Artificial intelligence has helped archaeologists uncover an ancient lost work of art.
The Nazca Lines in Peru are ancient geoglyphs, images carved into the landscape. First formally studied in 1926, they depict people, animals, plants, and geometric shapes. The formations vary in size, with some of the biggest running up to 30 miles long. Their exact purpose is unknown, although some archaeologists think they may have had religious or spiritual significance. Local guides believe the lines relate to sources of water.
Some Nazca lines span miles of Peruvian countryside. Flickr/Christian Haugen
New geoglyphs are still being discovered and can be hard to spot due to changes in the landscape, with natural erosion and urbanization breaking them up.
A research team from Yamagata University recently announced it had discovered 142 new Nazca formations, including images of birds, monkeys, fish, snakes, and foxes.
The team partnered with IBM to try and train its deep-learning platform Watson to look for hard-to-find geoglyphs.
They fed the AI with aerial images to see if it could spot any more Nazca outlines. Watson threw up a few candidates, from which the researchers picked the most promising. Sure enough, their field work confirmed the AI had found an ancient Nazca artwork.
The find was a relatively small depiction of a humanoid figure spanning just 16 feet. The researchers estimate the figure dates from roughly 100 BC to AD 100, making it at roughly 2,000 years old.
The project's success has prompted Yamagata University to announce a more prolonged partnership with IBM, and will create a full location map of the geoglyphs to help future archaeologists.
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Posted: at 11:44 am
Speaking at BlockShow Asia 2019, Todalarity CEO Toufi Saliba posed a hypothetical question to the audience: How many people would take a pill that made you smarter, knowing they can be controlled by a social entity?
No one raised their hand, and he was unsurprised.
Thats the response that I get, zero percent of you, he continued. Now imagine at the same time the pill has autonomous decentralized governance so that no one can control or repurpose that pill but the host yourself.
This time hands were raised in abundance. Decentralized governance represents a necessary step for the tech community to build up a trust in digital developments related to securely managing big data.
Economics and ethics can go together thanks to decentralization, commented SingularityNET CEO Ben Goertzel.
But does the decentralized governance represent a step forward from centralization, or it is just an illusion of evolution? Cole Sirucek, co-founder of DocDoc, shared his vision:
It is when we are at a point of centralizing data that you can begin to think about decentralization. For example, electronic medical records: in five years the data will be centralized. After that, you can decentralize it.
Goertzal didnt fully agree: I dont think it is intrinsic. The reason centralized systems are simpler to come by is how institutions are built right now. There is nothing natural about centralization of data. He elaborated on the mutual dependence of two important technologies:
Blockchain is not as complex as AI, but it is a necessary component of the future. Without BTC, you dont have means of decentralized governance. AI enables the future, blockchain secures it.
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