Category Archives: Artificial Intelligence
How Gainesville city officials are using artificial intelligence to improve its road conditions – Gainesville Times
Data-driven developer RoadBiotics developed the road-rating software, and its been implemented by municipalities in 34 states and 14 countries.
Public works director Chris Rotalsky said the software provides an algorithm that takes pictures of roadways every 10 feet, and rates the road by segments and points.
Utilizing this system provides several benefits to the Public Works Department, said Rotalsky. Staff time for data input is reduced, and the citys street network is evaluated within a short timeframe while applying the same visual criteria for analysis to each segment.
According to RoadBiotics representatives, its all done through labeled image data. Road images are captured from a cars windshield camera, and through-machine learning and digital paint brushing, the AI begins to scan the roads.
What does the AI look for when assessing each road image? The machine is designed to check for everything from unsealed cracks to cold patches to potholes before determining a final grade.
The system grades city roads on a five-level scale from green to red.Dark green roads are optimal and in the best condition. Yellow and orange roads, graded between 2, 3 and 4 respectively, are declining road conditions. And the worst-conditioned roads are coded red and are rated a 5.
According to the Pittsburgh-based company representatives, road ratings and reports are posted on an interactive, GIS-based platform called RoadWay.
Those ratings and reports help the city prioritize which roads need immediate improvement.
The data provided from RoadBotics is combined with other rating criteria such as base condition, ride condition to help determine repair and resurfacing actions needed for the street network, said Rotalsky.
In 2018, the city did its first road assessment using the software, with most of the citys roads graded as green or yellow.
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How Gainesville city officials are using artificial intelligence to improve its road conditions - Gainesville Times
Machine Learning Can Help The Insurance Industry Throughout The Process Lifecycle – Forbes
Artificial Intelligence
Insurance works with large amounts of data, about many individuals, many instances requiring insurance, and many factors involved in solving the claims. To add to the complexity, not all insurance is alike. Life insurance and automobile insurance are not (as far as I know) the same thing. There are many similar processes, but data and numerous flows can be different. Machine learning (ML) is being applied to multiple aspects of insurance practice.
Insurance is about risk. The insurance industry sets rates based on expected payouts so that, hopefully, they end up with positive revenue. Setting rates and understanding payout in order to maintain profitability is complex, and the industry hope is that ML can help in achieving that goal. Note, here, Im focusing more on ML than artificial intelligence (AI), because many of the complex statistical tools that are now considered ML can more efficiently accomplish some of the tasks than would neural networks, expert systems, or other purely AI tools.
There are multiple ways machine learning can help in the insurance industry. Let us take a look at three.
Health and life insurance are complex. There are multiple factors that go into understanding an individuals risk factors for disease, illness, and mortality. Insurance underwriters have historically used a core set of factors such as male/female, age, and smoker/non-smoker. When other factors have been used, such as zip code, the problem of red-lining has appeared in insurance as well as the more well-known area of financial red-lining. Therefore, there are regulations about how some demographic information must be used.
The need to address those legal concerns means that underwriting isnt only about individual health risks, but about legal risks as well. Analysis must be done to back out some features that could cause legal risk while still creating pools that remain profitable.
This is where machine learning comes in. The performance of modern computing can handle large amounts of data, and complex regression analysis can perform clustering that can help with analysis. Those ML techniques provide value without the need for AI. For insurance underwriting, statistical models and procedural code are providing an improvement in analysis for companies, said Paul Ford, CEO & Co-Founder, Traffk. We are working with neural network models, but the overhead for training and runtime must be balanced with the accuracy improvement necessary to make it worthwhile to roll-out those engines. While things might change, down the road, our existing ML models provide advances in analysis and profitability for our customers.
At the other end of the insurance process is the issue of claims. It is not only the insured who have problems with claims complexity. In the automotive industry, the need to understand the variety of repair options and parts available create a challenge for both service providers and the insurers.
With automotive claims, providing an estimate based on the typical costs for repair is not sufficient. Its not only that vehicle types vary, within a class of vehicle the repair costs can vary based on the insurance coverage, as well as the availability of parts in geographic regions.
Machine learning can help with claims in a number of ways. In addition, multiple ML tools can be used throughout the claims process.
Take the First Notice of Loss (FNOL), the initial notification to the insurer about the accident or damage. If theres a quick estimate of total loss, theres a different process flow that is much simpler. No ML is needed in the review of damage, but robotic process automation (RPA) might be used to simplify the claim flow to payment.
With other damage, or even to understand if there is a total loss, ML can be used. The most obvious tool is AI vision, but even this can have multiple processes. A phone app can step a customer through taking pictures that an AI system can then analyze for damage, with a backend AI system working to link to parts and estimate. A repair shop, in comparison to the insured, is more familiar with the process and can have a different front-end asking more detailed questions to more quickly get a more educated response from the repair experts.
Note that two different approaches were mentioned. It would be overly complex to have a single AI system that could support every step in the claims process. More efficiency is gained by letting separate systems process claims, identify damage and provide repair estimates, said Evan Davies, CTO, Solera. By using different approaches to machine learning through the claims process, you maximize the benefits of automation and enable skilled workers to focus on more complex cases.
One thing Evan Davies also pointed out was how the process flow can change depending on the severity of accident or the type of insurance coverage provided. Minor damage and standard coverage can be fully automated, as all parties are fairly comfortable with the process and dollar amounts. Totals, as mentioned, dont require AI. Those claims in the middle, however, can be helped with an adjuster reviewing the analysis and working with the customer, for the benefit of both short term monetary issues and long term customer relations.
Yes, we keep coming back to fraud. Sadly, it is a human condition and a risk in so many areas of business. Insurance is no exception. As Ive recently talked about fraud and ML in other business arenas, I wont go into detail here. Let it be sufficient to point out that analysis of claims doesnt stop at processing all claims as if they are proper.
Cluster analysis is used to understand, for instance, if a similar type of accident is happening in an area at above normal amounts; potentially indicating organized fraud.
In the analysis of potential fraud, multiple tools are used, some are in ML, such as statistics, rules based approached and even neural networks.
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Machine Learning Can Help The Insurance Industry Throughout The Process Lifecycle - Forbes
Startup bets on artificial intelligence to counter misinformation | TheHill – The Hill
U.K.-based fact-checking startup Logically launched a new service Monday aimed at helping governments and nongovernmental organizations identify and counter online misinformation using a blend of artificial intelligence and human expertise.
The Logically Intelligence (LI) platform collects data from tens of thousands of websites and social media platforms then feeds it through an algorithm to identify potentially dangerous content and organize it into narrative groups.
Over the last few years, the phenomenon of mis- and disinformation has firmly taken root, evolved and proliferated, and is increasingly causing real world harm, Lyric Jain, founder and CEO of Logically, said. Our intensive focus on combating these untruths has culminated in the development of Logically Intelligence, based on several years of frontline operations fighting against the most egregious attacks on facts and reality.
The company views the service as a way to help institutions, including social media platforms, to be quicker to react to burgeoning misinformation narratives.
Jain told The Hill that he hopes the platform will help information and intelligence sharing in the wake of the deadly insurrection at the Capitol earlier this year, which was planned in publicly-accessible online spaces but was seemingly missed by some authorities.
We think it's a really good time for us to be able to empower individuals to national governments with something like Logically Intelligence, he said in an interview, noting that the service could also help identify drivers behind coronavirus vaccine hesitancy.
LI provides users with a customizable Situation Room that organizes potentially dangerous pieces of content and shows links between them. For example, the platform could chart how a particular concept traveled from a fringe platform to a mainstream social media site, helping the user figure out to block off falsehoods before they proliferate.
It also identifies inauthentic accounts and can potentially be used to locate networks of them.
Logically touts its artificial intelligence and team of expert researchers as a differentiating factor that will help it be quicker and better at organizing content in useful ways.
What separates this from traditional social monitoring and internet monitoring tools is we then use all of the learning that we've done in terms of our artificial intelligence model, everything weve learned from our consumers products and projects weve worked on previous to this to then classify that content, global head of product Joel Mercer explained.
The platform also offers users several countermeasures once misinformation narratives have been detected, including investigative reports from Logicallys subject-matter experts, ways to flag content to platforms and built-in fact checks.
LI has been tested with some government agencies over the last year. The company worked with an undisclosed battleground state during the 2020 American election to identify misinformation and coordinated activity that might hurt election integrity.
It helped the state, according to Logically, push back on the false narratives by figuring out who was being targeted and boosting true information contradicting them through trusted local officials.
The company has built in safeguards aimed at ensuring the LI platform is not misused. The company has a list of permissible use cases and plans to monitor how the tool is being applied.
Logically, which was founded in 2017, has previously worked on a service focused on fact-checking news. It also produces research on misinformation, like the QAnon conspiracy theory.
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Startup bets on artificial intelligence to counter misinformation | TheHill - The Hill
Thinkwell Group Releases Its 6th Annual Guest Experience Trend Report on Artificial Intelligence and Its Future Impact in Location-Based Experiences -…
LOS ANGELES, Feb. 26, 2021 /PRNewswire/ --Thinkwell's 6th Annual Guest Experience Trend Report, released today, explores artificial intelligence (A.I.) and its applications in experiences, breaking down key takeaways and predictions for how A.I. can affect, adapt, and improve the guest experience in museums, theme parks, and beyond. With the prevalence of virtual assistants, smart home devices, and smart digital features in everyday life, the A.I. revolution is already here for consumers. At the same time, A.I. is also becoming more involved in our experiences, and there's no shortage of ideas for what A.I. can achieve and contribute to the guest experience.
With input from a representative sample of more than 1,300 survey respondents, Thinkwell's 6th Annual Guest Experience Trend Reportbreaks down three ideas around the guest experience and integration with A.I., while also exploring the rising demand for technology and personalization.
Three Big Ideas for A.I.:
"The potential to use A.I. for guest, brand, and operator benefit is limitless," says Craig Hanna, Thinkwell's Chief Creative Officer. "Thinkwell is focused on providing innovative, practical, and inclusive solutions to enhance the guest experience in any setting, and A.I. can play a big role in guest experiences and technology decisions as we look to the future."
For Thinkwell's insights, data highlights, and predictions on the future of artificial intelligence and guest experience, read the full report here.
With the collected survey data, Thinkwell has also developed an additional series of "deep dive" articles that breakdown A.I. and its intersection with other related topics and concerns, like racial bias, gender bias, and data security. The first two Deep Dive reports on Racial and Gender Bias and Age Gapsare available now.
Thinkwell's previous Guest Experience Trend Reports have covered a broad range oftopicsincluding: Fan Fests & Live Events, Guest Expectations in LBE, Virtual Reality, Branded IP Experiences, and Mobile in Museums & Cultural Attractions.
About Thinkwell Group
ThinkwellGroup is a global experience design and production agency with studios and offices in Los Angeles,Montral,AbuDhabi, and Beijing. For 20 years, our multi-disciplinary team has created compelling experiences for a wide range of clients and brands around the world.Thinkwell'screative, collaborative teams have extensive experience in the strategy, planning, design, and production of theme parks, destination resorts, major branded and intellectual property attractions, events & spectaculars, museums & exhibits, expos, and live shows. The award-winning company has become a leader in experiential design by bringing a unique holistic approach to every engagement. Thinkwellmost recently deliveredLionsgateEntertainment World and the award-winning Warner Bros. WorldAbuDhabi, the world's largest indoor theme park.Thinkwellcelebrates its 20th anniversary in 2021.
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Katherine Mitchell
Director, Marketing
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AAAI 2021: Accelerating the impact of artificial intelligence – Microsoft
The purposeoftheAssociation for the Advancement of ArtificialIntelligence, according to its bylaws, istwofold.The first is to promote researchin the area ofAI, and the second is to promote theresponsibleuseof these types of technology.Theresult was a35th AAAI Conference on Artificial Intelligence (AAAI-21)schedule thatbroadens the possibilitiesofAI andisheavily reflective ofa pivotal time in AI researchwhenexperts are asking bigger questions about how best toresponsiblydevelop, deploy, and integratethe technology.
Microsoft and its researchers have been pursuing and helping to foster responsible AI for yearsdeveloping innovative AI ethics checklists and fairness assessment tools like Fairlearn, establishing the Aether Committee to make principle-based recommendations, and laying out guidelines for human-AI interaction, to name only a few of the milestones in this area.
As a natural extension, researchers from Microsoft are presenting papers at this years AAAI that show the wide net theyre casting when it comes to developing responsible AI and using it for applications that do good. In How Linguistically Fair are Multilingual Pre-Trained Language Models?, researchers explore the fairness of current large multilingual language models across different languages. More specifically, they uncover how choices have been made about which models are fair and offer strategies for how these decision processes can be improved. Another paper demonstrates how AI can impact both specific industries and global challenges. In Where theres Smoke, theres Fire: Wildfire Risk Predictive Modeling via Historical Climate Data, researchers reexamine how AI can be used to predict wildfires by taking historical, climate, and geospatial data into account to improve modeling.
The belowselectionof AAAI-accepted papersshowcasesspecific advances with the potentialto havefar-reaching impact.AI that empowersallpeopleis the end goal, whether that bethroughbetter communication, better protection oftheirprivacy,orbetter optimization ofeverydayprocessesin specificfields.
For more on whatMicrosoft, asilver sponsor of the conference, and itsresearchers are undertaking when it comes to moving AI forward, explore more at theMicrosoft at AAAI 2021page.
In a nutshell: Reinforcement learning for order execution in quantitative investment.
Going deeper: The paper Universal Trading for Order Execution with Oracle Policy Distillation proposes a novel universal trading policy optimization framework for order execution in quantitative finance. It bridges the gap between noisy yet imperfect market states and optimal action sequences for order execution. Particularly, on one side, this framework leverages a policy distillation method that can better guide the learning of the common policy toward practically optimal execution by an oracle teacher with perfect information to approximate the optimal trading strategy. On the other side, a universal trading policy has been derived from the market data of various instruments, which is more training effective and more general to trade for different instruments.
Potential reach: This work can create an impact in the field of trading optimization in quantitative financial investment. The proposed universal learning-to-trade paradigm could substantially advance trading optimization with potentially significant profit gaining in order execution. The code is available in the Qlib project on GitHub.
First of its kind: To the best of the researchers knowledge, this is the first work to employ policy distillation in reinforcement learning to bridge the gap between imperfect noisy data and optimal action sequences. Moreover, the work shows that direct policy optimization has a great advantage over the traditional model-based financial methods and value-based model-free reinforcement learning methods.
Thepeopleand organizations involved:Kan Ren,Weiqing Liu,Dong Zhou,Jiang Bian, andTie-Yan Liufrom Microsoft ResearchAsia;Yuchen Fang,Weinan Zhang,andYong Yufrom Shanghai Jiao Tong University
Additional resources and related work:
In a nutshell: Want a translation system for languages with no written text? UWSpeech is your choice.
Going deeper: Existing speech-to-speech translation systems rely on the text of target language, and these existing systems cant be applied to unwritten target languages (languages without written text or phonemes). In the paper UWSpeech: Speech to Speech Translation for Unwritten Languages, researchers developed UWSpeech, a translation system for unwritten languages. UWSpeech converts target unwritten speech into discrete tokens with a converter. It then translates source-language speech into target discrete tokens with a translator and, finally, synthesizes target speech from target discrete tokens with an inverter. The researchers propose a method called XL-VAE in UWSpeech to enhance vector quantized variational autoencoder (VQ-VAE) with cross-lingual (XL) speech recognition, in order to train the converter and inverter of UWSpeech jointly.
Potential reach: This research sits broadly within cross-lingual speech translation, which can impact many scenarios where one spoken language needs to be translated into another. Conversations, lectures, international travel, and conferences are all examples where UWSpeech could be utilized. UWSpeech can also help to preserve unwritten languages spoken by a small amount of people.
Extended applications: Although this paper focuses on how UWSpeech can be applied to speech-to-speech translation, it can also be used to improve text-to-speech and speech-to-text translation, showing promising results in both areas. See the paper for more details.
The people and organizations involved:Xu Tan,TaoQin,andTie-Yan Liufrom the Machine Learning Group at Microsoft Research Asia;Chen Zhang, Yi Ren,andKejun Zhangfrom Zhejiang University
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In a nutshell: Watch out! Data augmentation could actually hurt privacy. Stronger membership inference attack reveals where we need to improve protection.
Going deeper: The paper How Does Data Augmentation Affect Privacy in Machine Learning? challenges a common belief that data augmentation can prevent overfitting and hence protect the model from leakage of individual data points. The researchers developed membership inference algorithms that employ augmented instances and achieve state-of-the-art success rates of attacking well-generalized models trained with data augmentation, showing that privacy risk in these deep learning models could be greater than previously thought. Revealing this vulnerability encourages future development of techniques to strengthen the privacy protections of data augmentation as a training method.
Potential reach: The new proposed membership inference algorithms can better evaluate the privacy risk of a model and can hence help prevent other privacy attacks.
Toward better privacy: The end goal is to make a privacy guarantee in real-world machine learning tasks practical.
The people and organizations involved:Huishuai Zhang,Wei Chen,andTie-Yan Liuof Microsoft Research Asia;Da Yu,intern atMicrosoft ResearchAsiaat the time of the workand student atSunYat-Sen University;Jian Yin, Professor atSunYat-Sen University
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In a nutshell:A two-branch convolutional neural network approach tointeractive speech and noise modeling for speech enhancement.
Going deeper: Mainstream deep learningbased speech enhancement mainly predicts speech only, ignoring the characteristics of background noises. However, traditional speech enhancement methods mostly go the opposite way, that is, they model noises with an assumption on noise distributions. The result is that their generalization capability is limited. In the paper Interactive Speech and Noise Modeling for Speech Enhancement, researchers propose the SN-Net, an interactive speech and noise modeling framework for speech enhancement, where speech and noise are simultaneously modeled in a two-branch deep neural network. Several interactions are introduced to help speech estimation benefit from noise prediction, and vice versa. As its challenging to model noises because of the diverse noise types, self-attention is employed in modeling both speech and noise. The SN-Net outperforms the state of the art by a large margin on several public datasets.
Potential reach:This technology can be widelyimpactfulin applications where speech clarity is important, including video recordings, online meetings, and virtual lessons.The research can naturally be extended to usewiththe speaker separation task (see paper for more on this).
Stateoftheart across multiple benchmarks:The researchers tested SN-Net againststate-of-the-artmodels on Voice Bank + DEMAND and the Deep Noise Suppression (DNS)challengedataset.Additionally, researchersconducted a two-speaker speech separation experiment onthe TIMITcorpus, and SN-Net outperforms Conv-TasNet, thestate-of-the-artmethod, for SDR (signal-to-distortionratio) improvement and Perceptual Evaluation of Speech Quality (PESQ). See the paper for a detailed breakdown of these tests.
The people and organizations involved:Xiulian PengandYan Lufrom the Media Computing Group at Microsoft Research Asia; Sriram Srinivasan from Microsoft; and Chengyu Zheng and Yuan Zhang from Communication University of China
Additional resourcesand related work:
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AAAI 2021: Accelerating the impact of artificial intelligence - Microsoft
Samsung has a line of artificial intelligence robots that help improve your daily life – Yahoo News
Bloomberg
(Bloomberg) -- A rout in Malaysian glove makers is deepening, sending valuations for some companies to rock-bottom levels.Top Glove Corp., the worlds biggest, slid 7.8% in a fifth day of declines Wednesday, taking its February loss to 22%, set to be the worst for any month on record. The stock is trading at about 6 times 12-month forward earnings, from a record high of 43 times in May. Supermax Corp., which surged 784% last year, is down 26% this month and trades at 4.6 times.More than $6 billion in market value has evaporated in February alone for Malaysias top four glove makers as global vaccine rollouts accelerate and short sellers swarm these pandemic winners. Malaysia is starting its own Covid-19 vaccination campaign Wednesday, further dampening sentiment.There is a narrative around re-opening which has definitely impacted sentiment, said Ross Cameron, a Tokyo-based fund manager of Northcape Capital Ltd., who has been investing in glove makers for more than a decade. But the fundamentals for the glove sector remain strong and valuations are as cheap as we have seen for a decade.The slump is a far cry from months ago when glove stocks became one of Asias hottest trades at the height of the global pandemic and helped spur a comeback by amateur investors. That helped catapult volumes in Malaysias stock market to record highs last year.Hartalega Holdings Bhd. is trading below 10 times estimated earnings, down from 60 in June, while Kossan Rubber Industries Bhd.s multiple has dipped to 4.1 times, according to data compiled by Bloomberg.Read more: These Covid Billionaire Fortunes Are Fading With Vaccine RolloutDespite the slump in these glove stocks, Cameron says their prospects remain strong and valuations simply do not reflect this reality.Stocks like Top Glove offer some of the highest sustainable dividend yields in the world, he said. The glove sector was an attractive sector for many years prior to the pandemic, and it will likely be even more attractive in the post-pandemic era.(Updates prices, ratios throughout.)For more articles like this, please visit us at bloomberg.comSubscribe now to stay ahead with the most trusted business news source.2021 Bloomberg L.P.
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Samsung has a line of artificial intelligence robots that help improve your daily life - Yahoo News
How Can Made in India Artificial Intelligence Revolutionize the World? – Analytics Insight
India is not only the worlds most populated nation, but also the youngest country in the world. The country is currently in the developing phase. But it has garnered global interest in terms of the fastest developing nation and digital adoption. This is largely because India has the 3rd largest startup ecosystem in the world, according to Startup India. And every year the number of startups in the country is snowballing. Like other countries, India is also making aggressive efforts to achieve the AI-led digital economy, with its AIforAll strategy.
The potential of artificial intelligence is not clandestine for todays enterprises. It frees up organizations from expensive resources for higher-level tasks. AI reduces the time taken to perform a task, enabling multitasking and easing the workload for existing resources. It facilitates decision-making by making the process faster and smarter. It has mass-market potential and can be deployed in any business scenario, for any work, and in every industry.
With keeping such capabilities in mind, countries across the world have built their AI strategies. AIforAll aims at enhancing and empowering human capabilities to address the challenges of accessibility, affordability, scarcity and inconsistency of skilled talent. It is designed for the effective implementation of artificial intelligence initiatives to develop scalable solutions for emerging economies.
As artificial intelligence refers to a machine that mimics human behaviour and ability, it is being used in manufacturing, healthcare, agriculture, education and skilling.
The IT and ITeS services segment is critical to Indias economy. In FY2020, the domestic revenue of the IT industry was estimated at US$44 billion and export revenue was at US$147 billion. The IT-BPM industrys revenue was estimated at around US$191 billion, growing at 7.7% year over year. It is expected to reach US$350 billion by 2025. On the other side, revenue from the digital segment is expected to grow by 38% of the total industry revenue by 2025, to reachUS$1 trillion(INR 69,89,000 crore) by 2025.
In the adoption of artificial intelligence technology, India is making good progress. Areport from Accenturesuggests that this technology can likely add US$957 billion, or 15% of Indias current gross value in 2035. This progress seems achievable as the countrys AI strategy emphasizes harnessing collaborations and partnerships, and aspires to ensure prosperity for all.
While India has been ranked second on the Stanford AI Vibrancy Index primarily for its large AI-trained workforce, leading technology institutes like the IITs, IIITs and NITs have the potential to be the cradle of AI researchers and startups. With Indias push for digitization and AI initiatives, private firms now are racing to win big contracts by spending huge capital to develop new technologies and spinning out new AI and data science-based startups.
Today, the country is on the way to be a major contributor to AI-powered solutions that address issues not only in India but around the world. For instance,HyperVerge, a B2B SaaS company, builds AI models for real-time image and video analysis. Its deep learning networks power applications for large businesses in financial services, telecom, energy, security and defense. Similarly, Indias eCommerce giant Flipkart is powering its decisions in ordering, distribution and product pricing on its platform using AI.
For big techies and firms, artificial intelligence can foster growth and profitability while transforming businesses. It can help drive innovation for entrepreneurs and young companies while improving public safety and advancing the quality of lives in society as a whole.
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UN forum unveils the wonders of artificial intelligence and other Science, Technology and Innovations for Africa – Africanews English
Africa could expand its economy by a staggering $1.5 trillion dollars, by capturing just 10% of the speedily growing artificial intelligence (AI) market, set to reach $15.7 trillion by 2030.
Vera Songwe, UN Under-Secretary-General and Executive Secretary of the Economic Commission for Africa (ECA), made the argument Thursday.
She was addressing several ministers and other participants at the third Africa Regional Science, Technology and Innovation Forum (ARSTI2021), through the Director of the Technology, Climate Change and Natural Resource Development Division of ECA, Jean Paul Adam.
AI growth can help in creating additional high value and decent jobs, diminish poverty, increase the productivity of firms, preserve the environment and foster better living conditions, she added.
Research has shown that AI has the potential to solve some of the most pressing challenges facing Africa and drive sustainable development in agriculture, health, infrastructure, financial and public services and climate change, Songwe maintained.
ECAs Executive Secretary said the Republic of Congo, which is hosting the Forum in situ in Brazzaville and online, finds itself in a special subregion, blessed with natural capital, such as huge forests. However, these forests have been disproportionately depleted, in comparison to those of other parts of the world, partly due to climate change. Artificial intelligence, she argued, could enhance already existing technologies which have been used to tackle COVID-19, to solve such climate change problems.
The imminent creation of an African Artificial Intelligence Research Centre in Brazzaville, Congo, with support from ECA, could give momentum to this new movement in Africa.
Lon Juste Ibombo, Congos Minister of Posts, Telecommunications and Digital Economy, praised ECA for its background work towards establishing the Centre, which, he said, demonstrates Africa is innovative and uninhibited.
The Centre is being designed to improve the current landscape of Artificial Intelligence research in Congo and in Africa in general, to orient the use of AI to foster economic and social development, while promoting close collaboration between academia and the industrial sector in AI and robotics across Africa.
According to Shamila Nair-Bedouelle, UNESCOs Assistant Director-General for Natural Sciences, it is such investments and strong partnerships for capacity building in science, technology and innovation which would accelerate the implementation of the Sustainable Development Goals (SDGs) in Africa.
Innovation cannot be decreed; it is planned and designed! enthused Arlette Soudan-Nonault, Minister of Tourism and Environment of the Congo.
Africa therefore has no excuse to be absent from the big rendezvous of innovation, which defines the 21st century, she warned, adding that university dons, economists and, industrialists must come together to lead todays leaners into this exciting world.
In such a world, the STI that we teach will determine what our continent will become, echoed Amon Murwira, Minister of Higher and Tertiary Education, Innovation, Science and Technology Development, Zimbabwe.
Our industry must emerge from our classrooms and laboratories, supported by the correct educational system design and framework which no longer teaches students about where they get things but how to make things, he insisted, as he cited his countrys Education 5.0 philosophy. The philosophy is sequenced on (i) teaching, (ii) research, (iii) community outreach, (iv) innovation and (v) industrialisation.
It is therefore up to Africa to take up this challenge of rapidly improving investments and attention to STIs, hinged upon its endowments in nature and biodiversity our surest guarantee said Parfait Aim Coussoud Mavoungou, Minister of Scientific Research and Technological Innovation of Congo, and incoming Chair of the Forum.
ARSTI2021 features several debates and breakout sessions to follow-up and review of progress made since the first two sessions of 2109 and 2020.
It has also featured an innovation bootcamp for young Africans, both in person in Brazzaville and connecting from across the continent and affording them an opportunity to develop projects using skills they have learnt in relation to robotics and AI, as well as access to 3D printing technologies.
By the time the Forum rounds off on Friday, it would have identified potential mechanisms and measures that African countries could deploy to leverage STIs for achieving the Sustainable Development Goals and the Aspirations of Agenda 2063.
Distributed by APO Group on behalf of United Nations Economic Commission for Africa (ECA).
Africanews provides content from APO Group as a service to its readers, but does not edit the articles it publishes.
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UN forum unveils the wonders of artificial intelligence and other Science, Technology and Innovations for Africa - Africanews English
Into the future: Tel Aviv University launches new artificial-intelligence center – JNS.org
(February 25, 2021 / JNS) Tel Aviv University has launched its new Multidisciplinary Center for Artificial Intelligence and Data Science on Wednesday during the universitys AI week to encourage research that uses the most advanced methods of both disciples.
The centers aim is to train a new generation of researchers and industrialists who will take Israel to the forefront of the global AI revolution in the coming years.
[AI] is expected to impact our way of life in every aspectfrom drug development and data-based personalized medicine to defense and security systems, financial systems, scientific discoveries, robotics, autonomous systems and social issues, said Professor Meir Feder, who will head up the center.
It is very important to train human capital in this area, and therefore, the center will provide all TAU students with basic AI education, he added.
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The center will work with hundreds of researchers that include scientists on campus and beyond, as well as collaborate with the military, public institutions, and leading universities and research institutes around the world, said Feder.
The new center follows the recent launch at TAU of a multidisciplinary epidemic research center. Other multidisciplinary centers, which will address climate change and aging, are in the planning stages, according to university president Ariel Porat.
The establishment of the AI center is one more step in implementing TAUs vision for advancing groundbreaking, multidisciplinary research that brings together the universitys finest researchers, the high-tech industry and the community, said Porat.
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Into the future: Tel Aviv University launches new artificial-intelligence center - JNS.org
The AI Infrastructure Alliance Launches With 25 Members to Create the Canonical Stack for Artificial Intelligence Projects – Business Wire
SAN FRANCISCO--(BUSINESS WIRE)--Today, the AI Infrastructure Alliance (AIIA), a non-profit organization with 25 global members officially launched with the mission to create a robust collaboration environment for companies and communities in the artificial intelligence (AI) and machine learning (ML) space. This global Alliance brings together top technologists across the AI spectrum and includes a wide range of member companies. Together, these companies and communities provide a glimpse into the future where AI creates real value for everyday businesses and not just big tech powerhouses.
Partnerships in the Alliance will help create a Canonical Stack for AI, by driving strong engineering standards and creating seamless integration points between various layers of the AI infrastructure ecosystem. Canonical means "a set of rules, standards and principles by which something is judged, and a Canonical Stack (CS) for AI will set the standard for how enterprises develop and design machine learning models at scale. It will let data scientists and data engineers move up the stack to solve more complex, higher order problems, instead of reinventing the wheel on every data science project.
The AI and ML space currently lacks a standard set of tools and solutions, blocking data science teams from sharing their work and collaborating across the world. Rather, there is wild proliferation of proprietary, cloud lock-in solutions that benefit individual companies, but not the data scientists and engineers building the AI applications of today and tomorrow. The Alliance came together to help those data science teams break out of lock-in so they can build on top of a standardized, open platform that works across all of their environments.
"Time and again, I've seen development teams get excited about the potential of AI to transform their business and applications, only for them to get stopped dead in their tracks by a fragmented and confusing array of technologies with little to no integration," said Dan Jefferies, Director of the AIIA. "Despite a massive surge of partial solutions, no single tool exists that lets teams leverage the true power and potential of AI. The AI Infrastructure Alliance will help create clarity in this confusing space by building a cohesive framework and bringing together leaders and innovators to help set the standard for how data science teams build models now, and into the future."
The AI Infrastructure Alliance provides a range of benefits to member companies including opportunity to:
Creating a place for top AI companies to work together will speed development of the infrastructure that businesses really need to make the promise of AI a reality, said Joey Zwicker, Co-Founder of Pachyderm, a founding member of the AIIA. As the Canonical Stack comes together, it will vastly reduce time to value for any company, in any industry, thats leveraging AI across their business.
Core founding members include Pachyderm, Seldon, Determined AI, Algorithmia, Tecton, ClearML by Allegro AI, Neu.ro, ZenML by Maiot, DAGsHub, TerminusDB, WhyLabs, YData, Superb AI, Valohai, Superwise.ai, cnvrg.io, Arize AI, CometML, Iguazio, UbiOps, and Fiddler. These companies have raised over $200M in collective venture capital funding from top firms including Andreessen Horowitz, Sequoia Capital, GV, Benchmark, NorWest Venture Partners, Madrona Venture Group and Gradient Ventures.
About the Artificial Intelligence Infrastructure Alliance
The Artificial Intelligence Infrastructure Alliance (AIIA) is a consortium of leading artificial intelligence startups with a mission to help every company realize the infinite potential of AI. Formed in February 2021, the Alliance is focused on tying together the complex web of existing AI technologies into a single Canonical Stack, providing the infrastructure on which any companyfrom tiny startups to global enterprisescan run impactful AI projects. Founding members include Pachyderm, Seldon, Determined AI, Algorithmia, Tecton, ClearML by Allegro AI, Neu.ro, ZenML by Maiot, DAGsHub, TerminusDB, WhyLabs, YData, Superb AI, Valohai, Superwise.ai, cnvrg.io, Arize AI, CometML, Iguazio, UbiOps, and Fiddler. To learn more about the AIIA or to become a member, visit https://ai-infrastructure.org/.