In general, AI has fundamentally changed the approach to search engine optimization. In this article, we will discuss using it for our purposes and learn the opinion of experts on the topic of AI.
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Amman-based Mawdoo3 has raised $10 million in fresh funds to close its Series B with $23.5 million, the startup announced today. The investment came from UK-based Kingsway Capital, US & Egypt-based Endure Capital, Endeavor Catalyst, Choueiri group’s investment arm Equitrust, and Amman-based AdamTech Ventures. The startup had raised $13.5 million as first tranche of this round in July last year.
Founded by Rami Al-Qawasmi and Mohammad Jaber, Mawdoo3 that is often labeled as Wikipedia of the Arab world is currently the largest Arabic website, receiving tens of millions of visitors every month. The website features over 150,000 articles on different topics ranging from lifestyle and food to health and education. The two had started the platform to enrich the Arabic content on the internet.
Dr. Mohammad Jaber, CO-Founder and Chief Operating Officer, said that this strategic investment will be used for launch of Ujeeb which is apparently a Quora-like digital Q&A platfrom that allows users to ask questions, and receive detailed answers from experts, specialists, and individuals who have shared similar experiences around the world, in Arabic. The platform is already live and has over 200,000 answers already. Mawdoo3’s team is aiming to take this number to 10 million within first year of the launch.
“We believe that Ujeeb will fulfill a vital need in the Arab world through a unique, more interactive type of Arabic content that Mawdoo3.com, our flagship website, does not cover. The Ujeeb platform was launched to complement the approach we had started with the establishment of Mawdoo3.com, allowing us to address both general and very specialized topics through a personalized knowledge-sharing experience,” said Jaber in a statement adding that Ujeeb will be supported by artificial intelligence and tools to optimize the users’ experience while browsing the platform on the basis of users’ interests and needs.
Patrick Thiriet, Chief Strategy Officer, Choueiri Group, said: “From the very beginning of our partnership with Mawdoo3.com, we’ve always been extremely impressed by the team’s constant learning journey and their passion to deliver Arabic content to the Arab population. Mawdoo3.com, and now Ujeeb, are a testament that there is a bright future for smart digital content platforms in the MENA region.”
Mawdoo3 last year had also launched Salma, a Siri-like Arabic AI assistant that uses Mawdoo3’s large database to answer different questions and allow users to consume Mawdoo3’s content in a new way. Salma can also be used by companies in different sectors to better interact with their customers through voice services.
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At essentials level, you can work under supervision to carry out a range of activities independently, monitor your own work in short timeframes and absorb technical information.
At foundation level you can carry out a broad range of tasks, use your initiative and schedule your own and other people’s work.
Prime Minister Narendra Modi will inaugurate a global virtual summit on Artificial Intelligence (AI), RAISE 2020 – ‘Responsible AI for Social Empowerment 2020’ on October 5, according to the Ministry of Electronics and Information Technology.
The summit is scheduled to be held from October 5 to 9, 2020 and is being organised by the Ministry of Electronics and Information Technology (MeitY) and NITI Aayog.
The inauguration event will take place in the presence of Minister of Electronics & IT, Communications and Law & Justice, Ravi Shankar Prasad, eminent global AI expert Professor Raj Reddy, Reliance Industries Ltd chairman Mukesh Ambani, IBM CEO Arvind Krishna among others.
According to the Ministry of Electronics & IT, Reddy will hold a session about developing voice-enabled AI that removes linguistic barriers on October 6, the second day of the summit. Former Infosys CFO Mohandas Pai, and Brad Smith, President & Legal Head, Microsoft Global will also participate in sessions.
RAISE 2020 will have a dedicated session on building inclusive AI that empowers one billion-plus Indians. The session will have Jenny Lay Flurrie, Chief Accessibility Officer of Microsoft sharing her views.
Anita Bhatia, Assistant Secretary-General, Deputy Executive Director, UN Women shall also deliver a keynote in this session, which will have an all-women panel and is being curated by UN Women.
The ministry said that more than 15,000 stakeholders so far from across academia, the research industry and government representatives from across the world have registered to participate in RAISE 2020.
Industry analysts predict that AI could add up to USD 957 billion to India’s economy by 2035, it said.
Israeli artificial intelligence (AI) vehicle inspection company Ravin.ai has raised $4 million in a seed financing round led by Pico Venture Partners with Shell Ventures and private investor Adam Draizin. The is the first investment in an Israeli startup by Shell Ventures, the investment arm of energy major Royal Dutch Shell plc.
With offices in Haifa and London, Ravin.ai’s technology is used to autonomously inspect vehicles for damage using a smartphone or CCTV camera. The startup will initially target car rental companies but also hopes to extend operations to commercial fleets and the used car market.
Ravin.ai CEO and cofounder Eliron Ekstein said, “Ravin’s mission is to bring trust and transparency to the often stressful process of a car changing hands – whether it’s buying a used car or renting a car for the weekend. Our first customers are already seeing the benefits of using our technology, and we look forward to rolling out additional partnerships over the next several months.”
Ravin says it has commercial partners across the US and Europe, including Avis’ Heathrow Airport operations, and intends to use the new funding to further develop its technology products and to expand commercial activities across North America, Europe and Asia.
Published by Globes, Israel business news – en.globes.co.il – on May 21, 2019
© Copyright of Globes Publisher Itonut (1983) Ltd. 2019
In an effort to support and spur America’s artificial intelligence, President Donald Trump will sign an executive order to establish the American AI Initiative on Monday.
The field of artificial intelligence research was founded as an academic discipline in 1956. Despite a history of 60 years, the era is still at the very beginning, and the future has a bumpy road ahead when compared to similar disciplines, which is mainly driven by challenges in the domain of ethics and availability of data.
Fluctuating Fortunes of AI Since its beginning, Artificial Intelligence has experienced three major breakthroughs and two periods of stagnation. Its most recent renaissance was triggered in 2016 with the historical moment of AlphaGo defeating the world’s best players of Go, a game thought to be too complex for Artificial Intelligence.
As we learned from the previous circles of AI, whenever it makes a leap forward, there is a lot of scrutiny and concern over what this means for the world; both in the industry as well as society. As a result, certain ideas for AI have become highly controversial in the public and enters a “Trough of Disillusionment”.
Thinking about why Artificial Intelligence remains so controversial, it turns out that there is a significant gap between the expectations of what AI can provide, and what it is able to accomplish in reality. Today’s truth real-world examples of AI are still rare and often focus on very niche cases, far away from scenarios marketers write to chase clicks on social. There is still a long way to go before AI goes mainstream. As we are not lacking visions in this domain, we see signals of doubts about what AI can truly accomplish today. Now towards the end of this third rise of Artificial Intelligence, the fate of this emerging field is uncertain — again.
Data Famine Is Coming The most recent rise of AI was largely fueled from the availability of big data, which has powered the development of deep-learning in areas such as facial recognition, which can be considered as one of the main breakthroughs of this AI wave. In more complex fields, such as disease diagnosis, deep learning still faces challenges in bridging the gap between businesses and institutions. A major issue in this field is the accessibility of data From a holistic perspective, the data is available, but for several reasons not assessable. A common problem is that data is stored within silos. These silos often are a result of physical separation within companies internal network or even within the companies themselves. Another prominent issue is data structure incompatibility. As a result, there is no centralized data hub to train a powerful neural network via deep learning mechanisms. Cloud-based computing is often cited as a potential solution to the data silo problem, but it has proven to be expensive and time-consuming for large amounts of data. And then there are still the increasingly stringent data privacy regulations, such as General Data Protection Regulation. While such policies are important towards protecting the privacy of consumers, they also place heavy constraints on the usage of data and require to rethink how to build Artificial Intelligence applications in a compliant way.
Federated Learning — the promise of a 4th big breakthrough Consumer protection practices and data privacy are non-negotiable, and the bottom line to establish the needed trust. On the other side, it brings the risk of a data famine and a slowdown of the rise of AI. Federated Learning is a new approach to Artificial Intelligence that has the potential to bring the next big breakthrough in AI and overcome the data privacy and trust challenges of this wave. It is a machine learning framework that allows users to train machine learning models using multiple datasets distributed across a variety of locations while preventing data leakage and follow stringent data privacy regulations. In practice, Federated Learning has three major categories, depending on the distribution characteristics of the data.
Horizontal federated learning divides datasets according to features and is typically implemented in cases in which features overlap more than users. For example, three logistics companies operating in different regions may keep similar data on their consumers, but the overlap between consumers themselves is relatively small. Since their features are almost identical, users with the same features can be extracted to train models.
Vertical federated learning is generally used when multiple datasets have a large overlap of users but have different features. For example, a food delivery service and a hospital operating in the same area are likely to have a similar set of users, but keep track of different information between each: the hospital keeps track of health data, while the food delivery service tracks things like browsing habits and purchasing data. Vertical federated learning aggregates all of these features in order to build a model for both parties collaboratively.
When there is very little overlap between both the users and features of a dataset, federated transfer learning is used to overcome this lack of data or labels. Take, for example, a manufacturer in China and a logistics provider in the USA. Since they are geographically constrained, there is very little overlap between users; likewise, since they are different types of institutions, their features also have very little overlap. In such cases, transfer learning should be applied in conjunction with federated learning to define common representation between datasets and improve the overall performance of a model.
Despite its capabilities, an effective framework alone is not enough to completely address the challenges. Federated Learning must be developed into a commercial application that offers a flexible, win-win business model for a certain industry. By aggregating multiple isolated datasets across different institutions, federated learning makes it possible to develop an ideal model without the need to infringe on the privacy of each individual. Simplified spoken, it’s a method of training an algorithm with Data from multiple stakeholders by keeping the data in silos — Data Sharing Economy where data holders benefit by sharing their data, while the application providers can profit by providing the services necessary to the development of those models.
Cyrano Chen, Zion Chen and Michael Renz
The artwork is reportedly the “first portrait generated by an algorithm to come up for auction.” Last year, British auction company Christie’s grabbed he…