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From April 25 to 26 local time, the AI & Big Data Expo Global 2019 was held in OlympiaLondon. This is a two-day expo showcasing the next generation of technologies and strategies in AI and big data, attracting more than 4,000 relevant practitioners. Dr. Wei Cui, co-founder and chief scientist of Squirrel AI Learning, was invited to give a speech at the Expo and he also shared his insights into AI with senior executives from Darktrace, a UK cyber security company; SAP, the world’s third largest independent software providerReddit, one of the largest social platforms in the United States; and Just-Eat, a Danish company equivalent to “Meituan”.

The themes of this expo include: enterprise AI, AI and Internet of Things data analysis, big data business solutions and AI technology solutions.

Dr. Wei Cui from Squirrel AI Learning: How Does AI Provide Economical and Individualized Education for Every Family in China

Dr Wei Cui, chief scientist of Squirrel AI Learning, introduced the AI adaptive learning system independently developed by Squirrel AI Learning. This system can continuously monitor and evaluate students’ individual abilities, find their weaknesses in learning and allow students to progress at their own pace so as to improve their learning. The system provides optimized learning solutions and simultaneous counseling to maximize learning efficiency and improve students’ ability to acquire knowledge and skills.

For years, the lack of senior teachers and geographical problems have adversely affected the popularization of quality education in China. Squirrel AI hopes to train “super teachers” through AI and provide one-on-one tailored education for students.


In his speech, Dr. Wei Cui introduced the basic technologies used to build the system and carry out performance evaluation experiments. Squirrel AI Learning’s adaptive educational engine includes three layers of architecture: ontology layer, algorithm layer and interactive system. Content-focused, the ontology layer incorporates learning maps and knowledge maps. Squirrel AI Learning independently developed the technology to disassemble knowledge points at a super-nano level, making for more accurate determination of the knowledge points students are supposed to master. Take mathematics of junior high school as an example. Squirrel AI Learning can disassemble the 300 knowledge points into 30,000.

The algorithm layer includes content recommendation engine, students’ user portrait engine and target management engine. Based on user status evaluation engine and knowledge recommendation engine, Squirrel AI Learning will build a data model to detect the gaps of knowledge for each student accurately and efficiently and then recommend corresponding learning content according to these gaps.

The interactive system collects interactive data to learn more about students and improve the algorithm. Squirrel AI Learning cooperated with Stanford Research Institute to study the machine-student interactive system. Its self-developed MIBA student behavior data acquisition system won big award at the World Conference on AI.

In addition, the MCM system developed by Squirrel AI Learning can disassemble students’ model of thinking, capabilities and methods of learning and then provide training of these abilities and methods in a single subject according to students’ learning status.

By the beginning of this year, Squirrel AI Learning has set up nearly 2,000 learning centers in more than 300 cities across China with nearly 2 million registered student users. Last year, Squirrel AI donated 1 million free study accounts to underprivileged families to promote education fairness.


Marc Teerlink, SAP’s Global Vice President: How to Realize “Gold Rush” in the Era of AI

Marc Teerlink, SAP’s global vice president, talked about how enterprises adapt to the era of AI. The world is currently on the verge of a “gold rush” with AI. Teerlink’s speech is about how AI and machine learning bring wealth to enterprises as well as experience sharing by current industry leaders.

SAP estimates that by 2030, more than 60% of jobs will face great changes. About 51% of work will be automated but only 5% will be completely done by machine. Therefore, Teerlink prefers to call this era the era of “augmented intelligence” in which technology is used to enhance human’s ability to process information.

Teerlink said that partners using SAP machine learning software have translated algorithms into commercial profits. He cited one example: VALE, a Brazilian-based global mining company, used machine learning to optimize its procurement application process.

The current process is a purely manual one in which scattered information is distributed in multiple files and systems. As a result, 25-40% of the purchase requisitions are rejected every month due to errors, resulting in severe rework.


In the past few years, VALE has started to use SAP Leonardo open innovation framework based on design thinking and technology to define a re-conceived application process that provides an SAP Fiori application accessible from any device to help users complete the end-to-end process without logging into any back-end device systems.

Machine learning for image recognition is the core of this process. Image recognition algorithm is integrated into the application of SAP Leonardo machine learning so that maintenance technicians can recognize the serial number of the materials of any parts that need to be replaced by taking pictures of them. Even without Internet access, technicians can still take pictures of the parts and complete the purchase application process later.

Once the parts are identified, the application will connect to the back-end system and find the correct procurement process for the material, be it contract process or purchase requisition, and then the application will automatically complete the procurement requisition process and check whether the parts have been requested on the previous shift or exist in any nearby deposits.

This procedure streamlines the procurement application process, reduces the delivery cycle of procurement, reduces spare parts inventory, thus reducing working capital and improving labor efficiency.

Enterprises like VALE that have long engaged in AI have already tasted the sweetness. SAP observes that these enterprises generally have the following characteristics: the strategic center of C-level executives, increased competitive differentiation, new income and profitability, and strategies covering the whole field. They all view data as important assets.


Dave Palmer, Technical Director of Darktrace: How Does AI Affect Cybercrime

Darktrace is a British start-up in network security that mainly provides “corporate immune system” that can be deployed in company network to monitor network anomalies. Once suspicious behavior occurs within the network, Darktrace will remind IT managers and, when necessary, automatically trigger protection behavior to mitigate network attacks. Unlike traditional methods that rely on rules or signatures, this automated technology enables security teams to focus on high-value tasks and even to counter fast-moving automated attackers.

Dave Palmer believes that AI will greatly increase the impact of cyber crime on enterprises. Due to the open source environment of computer science and the introduction of various API & SDK by large companies, even people without relevant backgrounds can easily acquire and use AI technology such as face recognition and voice recognition, which greatly lowers the threshold of cyber crime.

Unlike planting a virus or malicious software in the system in the past, network attacks now take many forms and are more and more extensive. For example, stealing your data for blackmail, monitoring important meetings of competing companies, or modifying your data from the bottom to influence the decision-making of your superiors, etc.

Therefore, many companies are deeply engaged in network security protection. For example, Microsoft launched its cloud-based risk security detection tool in 2017 with which developers find bugs and other security vulnerabilities in software to be released or used. The tool is designed to fix bugs before software vulnerabilities occur.


Anand Mariappan, Senior Director of Reddit: Development History of Reddit Machine Learning

Anand Mariappan, senior director of Reddit in charge of search and machine learning engineering, reviewed the history, current projects and future direction of Reddit’s machine learning that covers data platforms, feed rankings, recommendations, user and channel similarities.

Reddit, the US version of “Tianya” and “Baidu Tieba”. According to the data released by Alexa, Reddit is the fifth largest website in the United States, ranking 14th in the world and even surpassing Facebook in traffic. Reddit currently has 330 million active users, nearly 140,000 active communities, 12 million posts and nearly 100 million comments per month. In February this year, Tencent invested $150 million into Reddit. Reddit is currently valued at $3 billion.

Reddit has been building and improving data pipelines over the past few years. Since 2014, it has been using Amazon S3 and Hive to gradually build a multilevel database architecture based on MIDAS, and now the architecture is based on Google’s BigQuery.

Reddit has 140,000 sub-Reddit, which can also be understood as channels. Recommending relevant channels to users is an important way to increase user participation, which was done through manual selection. Now deep learning takes the place of manual selection. Through deep learning, Reddie can directly gather all comments in a channel into a file and then use the end-to-end doc2vec model to train and get semantic information to assist in the matching.


Reddit also optimized the recommendation on the home page. It uses large-scale logistic regression algorithm to make personalized content recommendation based on parameters such as time, channel, user interest, and device.

Mariappan said Reddit is currently developing machine learning programs to optimize personalized models, and has achieved amazing results in the early development phase by using models on TensorFlow to improve the quality of content recommendation.

Ben DiasHead of Royal Mail: From Zero to Data Science

Ben Dias, head of analysis and data science at Royal Mail, shared his experience of “from zero to data science” and summarized seven key points, hoping to help enterprises accelerate their progress in developing data science by providing practical skills, tools and technologies.

First, be prepared. Enterprises should first understand themselves and be fully prepared in areas of data processing, standard business intelligence analysis, underlying architecture and technology stack.


Second, lay more emphasis on retention of talents than recruitment. Don’t rush to look for talents outside the enterprise. Instead, enterprises should train and retain talents and nurture suitable office culture.

Third, don’t hire “super chicken”. Super chicken refers to highly talented and motivated employees. Margaret Heffernan, an expert in business management consulting, pointed out in a TedTalk that a team of geniuses will not be more efficient, but rather has disastrous performance. Successful teams do not need superstars but collaborative staff working based on consensus.

Fourth: Don’t put all eggs in one basket. Enterprises should comprehensively consider short-term benefits, medium-term considerations and long-term planning.

Fifth, adopt the model of Lean StartupLean Startup is a method of developing business and products, aiming to shorten product development cycle and quickly find out whether the proposed business model is feasible. This model is achieved through a combination of business hypothesis-driven experiments, iterative product release and proven learning.

Sixth & Seventh: You must change everyone and everything & apply scientific methods to everything.


Gilles Comau, Head of Just Eat AI: Challenges and Opportunities of Building Personalized Strategies.

Gilles Comau is Just Eat’s director of machine learning and AI. Just Eat, founded in 2001, is a take-out ordering website in Denmark. It provides applications to enable consumers to easily place orders and make payment. Now it has operations in many countries around the world. In 2014, Just Eat was successfully listed on the London Stock Exchange with a market value of $2.4 billion.

Comau’s speech is centered on the challenges and opportunities of building personalized strategies. Delivery services involves millions of similar but greatly different products. Delivery areas also have geographical limitations, which needs to be optimized through algorithms.

Just Eat’s big data analysis helps predict what kind of food users will order at a particular time. For example, big data generated by Just Eat enables analysts to predict which regions are most likely to order healthy food and which regions prefer food collection to delivery.

Results of big data analysis of users’ eating patterns and trends will be provided to restaurants to help them meet various needs and increase menu items. This can help them grow their business.


Just Eat has more than 60 million accounts and at least 7.5 million people have multiple accounts. Therefore, Just Eat needs to use data science to delete repeated accounts and link users with similar attributes.

The match between restaurants and users helps users find delicious food more conveniently. The restaurant’s attributes are mainly based on the food it mainly recommends, including the flavor, attributes, taste, and ingredients of food. User profile is determined by ordering habits, preferences, social attributes, trading habits, contact information, etc.

These attributes will help Just Eat build a two-dimensional and visual vector search space. When searching delicious food through key words, users can get what they want just by judging which vector their key words are the closest to.

Machine learning is also used to deliver orders to customers fast through prediction of driver paths and improvement of communication efficiency so as to deliver food fast, maintain correct delivery order and prevent lost delivery orders.

SOURCE Squirrel AI Learning


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Invitation to presentation of EQT AB’s Q1 Announcement 2024




STOCKHOLM, April 5, 2024 /PRNewswire/ — EQT AB’s Q1 Announcement 2024 will be published on Thursday 18 April 2024 at approximately 07:30 CEST. EQT will host a conference call at 08:30 CEST to present the report, followed by a Q&A session.

The presentation and a video link for the webcast will be available here from the time of the publication of the Q1 Announcement.

To participate by phone and ask questions during the Q&A, please register here in advance. Upon registration, you will receive your personal dial-in details.

The webcast can be followed live here and a recording will be available afterwards.

Information on EQT AB’s financial reporting


The EQT AB Group has a long-term business model founded on a promise to its fund investors to invest capital, drive value creation and create consistent attractive returns over a 5 to 10-year horizon. The Group’s financial model is primarily affected by the size of its fee-generating assets under management, the performance of the EQT funds and its ability to recruit and retain top talent.

The Group operates in a market driven by long-term trends and thus believes quarterly financial statements are less relevant for investors. However, in order to provide the market with relevant and suitable information about the Group’s development, EQT publishes quarterly announcements with key operating numbers that are relevant for the business performance (taking Nasdaq’s guidance note for preparing interim management statements into consideration). In addition, a half-year report and a year-end report including financial statements and further information relevant for investors is published. Finally, EQT also publishes an annual report including sustainability reporting.

Olof Svensson, Head of Shareholder Relations, +46 72 989 09 15
EQT Shareholder Relations, [email protected]

Rickard Buch, Head of Corporate Communications, +46 72 989 09 11
EQT Press Office, [email protected], +46 8 506 55 334

This information was brought to you by Cision


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Kia presents roadmap to lead global electrification era through EVs, HEVs and PBVs



  • Kia drives forward transformation into ‘Sustainable Mobility Solutions Provider’
  • Roadmap enables Kia to proactively respond to uncertainties in mobility industry landscape, including changes in EV market
  • Company to expand EV line-up with more models; enhance HEV line-up to manage fluctuation in EV demand
    • Goal to sell 1.6 million EVs annually in 2030, introducing 15 models
    • PBV to play a key role in Kia’s growth, targeting 250,000 PBV sales annually by 2030 with PV5 and PV7 models
  • Kia to invest KRW 38 trillion by 2028, including KRW 15 trillion for future business
  • 2024 business guidance : KRW 101 tln in revenue with KRW 12 tln in operating profit; operating profit margin of 11.9% on sales of 3.2 million units globally
  • CEO reaffirms Kia’s commitment to ESG management

SEOUL, South Korea, April 5, 2024 /PRNewswire/ — Kia Corporation (Kia) today shared an update on its future strategies and financial targets at its CEO Investor Day in Seoul, Korea.

Based on its innovative achievements in the years since the announcement of mid-to-long-term business initiatives, Kia is focusing on updating its 2030 strategy announced last year and further strengthening its business strategy in response to uncertainties across the global mobility industry landscape.

During the event, Kia updated its mid-to-long-term business strategy with a focus on electrification, and its PBV business. Kia reiterated its 2030 annual sales target of 4.3 million units, including 1.6 million units of electric vehicles (EVs). The 2030 4.3 million annual sales target is 34.4 percent higher than the brand’s 2024 annual goal of 3.2 million units.

The company also plans to become a leading EV brand by selling a higher percentage of electrified models among its total sales, including hybrid electric vehicles (HEV), plug-in hybrid (PHEV), and battery EVs, projecting electrified model sales of 2.48 million units annually or 58 percent of Kia’s total sales in 2030.

“Following our successful brand relaunch in 2021, Kia is enhancing its global business strategy to further the establishment of an innovative EV line-up and accelerate the company’s transition to a sustainable mobility solutions provider,” said Ho Sung Song, President and CEO of Kia. “By responding effectively to changes in the mobility market and efficiently implementing mid-to-long-term strategies, Kia is strengthening its brand commitment to the wellbeing of customers, communities, the global society, and the environment.”


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BioVaxys Technology Corp. Provides Bi-Weekly MCTO Status Update




VANCOUVER, BC, April 4, 2024 /PRNewswire/ — BioVaxys Technology Corp. (CSE: BIOV) (FRA: 5LB) (OTCQB: BVAXF) (the “Company“) is providing this bi-weekly update on the status of the management cease trade order granted on February 29, 2024 (the “MCTO“), by its principal regulator, the Ontario Securities Commission (the “OSC“), under National Policy 12-203 – Management Cease Trade Orders (“NP 12-203“), following the Company’s announcement on February 21, 2024 (the “Default Announcement“), that it was unable to file its audited annual financial statements for the year ended October 31, 2023, its management’s discussion and analysis of financial statements for the year ended October 31, 2023, its annual information form for the year ended October 31, 2023, and related filings (collectively, the “Required Annual Filings“). Under National Instrument 51-102, the Required Annual Filings were required to be made no later than February 28, 2024.

As a result of the delay in filing the Required Annual Filings, the Company was unable to file its interim financial statements for the three months ended January 31, 2024, its management’s discussion and analysis of financial statements for the three months ended January 31, 2024, and related filings (collectively, the “Required Interim Filings“). Under National Instrument 51-102, the Required Interim Filings were required to be made no later than April 1, 2024.

The Company anticipates filing the Required Annual Filings by April 30, 2024. The auditor of the Company requires additional time to complete its audit of the Company, including the Company’s recent acquisition of all intellectual property, immunotherapeutics platform technologies, and clinical stage assets of the former IMV Inc. that closed on February 16, 2024. In addition, the Company anticipates filing the Required Interim Filings immediately after the filing of the Required Annual Filings.

Except as herein disclosed, there are no material changes to the information contained in the Default Announcement. In addition, (i) the Company is satisfying and confirms that it intends to continue to satisfy the provisions of the alternative information guidelines under NP 12-203 and issue bi-weekly default status reports for so long as the delay in filing the Required Annual Filings and/or Required Interim Filings is continuing, each of which will be issued in the form of a press release; (ii) the Company does not have any information at this time regarding any anticipated specified default subsequent to the default in filing the Required Annual Filings and Required Interim Filings; (iii) the Company is not subject to any insolvency proceedings; and (iv) there is no material information concerning the affairs of the Company that has not been generally disclosed.

About BioVaxys Technology Corp.


BioVaxys Technology Corp. (, a biopharmaceuticals company registered in British Columbia, Canada, is a clinical-stage biopharmaceutical company dedicated to improving patient lives with novel immunotherapies based on the DPX™ immune-educating technology platform and it’s HapTenix© ‘neoantigen’ tumor cell construct platform, for treating cancers, infectious disease, antigen desensitization, and other immunological fields. The Company’s clinical stage pipeline includes maveropepimut-S which is in Phase II clinical development for advanced Relapsed-Refractory Diffuse Large B Cell Lymphoma (DLBCL) and platinum resistant ovarian cancer, and BVX-0918, a personalized immunotherapeutic vaccine using it proprietary HapTenix© ‘neoantigen’ tumor cell construct platform which is soon to enter Phase I in Spain for treating refractive late-stage ovarian cancer. The Company is also capitalizing on its tumor immunology know-how and creation of a unique library of T-lymphocytes & other datasets post-vaccination with its personalized immunotherapeutic vaccines to utilize predictive algorithms and other technologies to identify new targetable tumor antigens. BioVaxys common shares are listed on the CSE under the stock symbol “BIOV” and trade on the Frankfurt Bourse (FRA: 5LB) and in the US (OTCQB: BVAXF). For more information, visit and connect with us on X and LinkedIn.


Signed “James Passin
James Passin, Chief Executive Officer
Phone: +1 646 452 7054

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