The Japanese MNC Canon has developed an artificial intelligence-powered smile detecting camera system. The technology has been installed in Canon’s subsidiary office in China with the intention of tackling workplace morale issues.
The camera system allows only ‘smiling’ employees to enter the office and organize meetings to ensure that the workers are happy throughout the working hours. Last year, Canon Information Technology announced the launch of its smart smile recognition system as a part of its workplace management tools.
However, the technology didn’t receive much attention until it got implemented in China. Canon Official said this technology was implemented to bring cheerfulness to the work floor in this COVID-19 pandemic. The official further added that they have been wanting to encourage workers to build a positive environment using this technology.
However, a report from Financial Times shows the degree to which big enterprises in China track the activity of their employees using artificial intelligence. An IT engineer from Shanghai said that this technology makes no sense as it is not possible for a person to stay in the same frame of mind throughout the day.
King’s College London academic Nick Srnicek said that the employees are not being replaced by artificial intelligence. Instead, the companies are closely monitoring the workers using artificial intelligence. He added that the situation is quite similar to the time of the industrial revolution where technology was increasing the pace of the employee who works with machines, instead of the other way around.
Because of the ongoing pandemic, most of the companies are resorting to a work from home approach, leading to the adoption of surveillance software. According to Financial Time’s report, the artificial intelligence-powered smile recognition camera system is the least dangerous type of surveillance tool.
On Wednesday, Facebook and Michigan State University (MSU) announced that their new artificial intelligence technique could detect and pinpoint the generative model used to create deepfake images or videos.
Deepfake is creating fake images/videos from an existing image using deep learning techniques. It has become so efficient that it’s almost impossible to tell whether a picture or video is original or a deepfake.
Although Facebook has banned and eliminated deepfake in January 2020, it still seems to be a threat to the security of the users. Being the most widely used social media platform, Facebook is the place of residence for deepfake.
Facebook employed a new way for the detection of deepfake called ‘Fingerprint Estimation Network’. This method is based on the generative fingerprints that are created during the modeling of deepfake. These fingerprints are very similar to human fingerprints; they are unique and can trace the generative models that made a particular deepfake. The technique picks them up from the deepfake image or even from a video frame and traces them back to the original model.
Once the generative fingerprints are detected, this new technique uses a reverse-engineered method called the ‘model praising approach’ to detect the original generative model. Since most of the generative models that are used to fabricate deepfake are known, this method works perfectly fine; although the generative model is unknown, the AI can still see it, says the Facebook research team.
To test the efficacy of the new AI technique, Facebook put up 100,00 deepfakes into testing that were created using 100 generative models. Facebook says that this methodology developed, along with MSU, is working significantly better than the earlier ones used to detect and remove deepfakes.
Facebook and MSU research teams have also mentioned that they are putting a forethought to open-source the datasets, code, and the trained models used to detect the generative model to ease up the area of research of deepfake detection.
Engineers at the Swiss Center for Electronics and Microtechnology (CSEM) announced they have developed an artificial intelligence-powered chip that can perform complicated operations like voice, face and gesture recognition, and cardiac monitoring, which can run on solar energy.
This technology can eliminate artificial intelligence’s mandatory requirement of staying continuously connected to the cloud. This technology performs all artificial intelligence operations locally on the chipset rather than on the cloud. It is a customizable system that can be modified according to the requirements of any application, which requires real-time signal and image processing.
The technology will be unveiled at the 2021 VLSI Circuits Symposium in Kyoto this month. The technology works through an entirely new signal processing architecture that reduces the power consumption of the chip. It has an ASIC chip and a RISC-V processor, which also has been developed by CSEM. The technology will also have two coupled machine learning accelerators. The first machine learning accelerator is a binary decision tree (BDT) engine that can carry out simple tasks but cannot perform facial-voice-gesture recognition operations.
The second accelerator is a convolutional neural network (CNN) engine capable of performing complicated recognition operations efficiently. This unique approach reduces the power consumption drastically as the first accelerator does most of the job. Stephane Emery, Head researcher at CSEM, said, “When our system for example is used in facial recognition applications, the first accelerator will answer preliminary questions like are there people in the images? And if so, are their faces visible?”
This groundbreaking invention can revolutionize the field of artificial intelligence and machine learning as the chips developed by CSEM can run independently for more than one year. Additionally, it considerably reduces the maintenance and installation cost of such devices and enables the usage of them at locations where it is difficult to find a power source.
Indian Institute of Technology Mandi, along with IIT Mandi iHub and HCI foundation, is organizing a 6-day weekend workshop on Deep Learning Crash Course (ADLCC 2021) for Executives and Working professionals between 3rd July 2021 and 18th July 2021.
This crash course will cover both theory and practical sessions on artificial intelligence and machine learning. The theory section will be covered from 9 AM to 1 PM, and the practical sessions will be organized from 2 PM to 6 PM on weekends. An assessment will be conducted after the completion of the course, based on which the applicants will receive certificates.
Emphasis will be given on topics like Basics of Machine learning & Neural Networks, Object Localisation and Detection, Convolutional Neural Networks, Autoencoders and Variational AutoEncoder, and Generative Adversarial Networks. Dr. Aditya Nigam, Workshop Coordinator, and Assistant Professor, School of Computing and Electrical Engineering, IIT Mandi, said, “This workshop will be the key to enter the mystic world of AI/ML. Extensive learning has been planned through comprehensive sessions organized by various experts.”
He also added that the unique structure of the workshop would help mature learners understand the topics in a better way. The course will include sessions by venerated speakers like Dr. Varun Dutt, Dr. Chetan Arora, Dr. Kamlesh Tiwari, and many more. “This workshop is the first of its kind sponsored by the iHub and IIT Mandi, and the entire workshop series includes six such workshop events in 2021 in total,” said Dr. Varun Dutt of IIT Mandi. He also mentioned that in this workshop, the applicants would get hands-on experience of artificial intelligence and machine learning topics of various sectors, including the field of human-computer interaction.
The registration fee structure of the workshop is as follows –
The workshop has a limited number of seats, and the applications will be processed in a first-come, first-serve manner. Interested learners can apply for this workshop through IIT Mandi’s website.
Amazon Web Service (AWS) and Ferrari announced an agreement for AWS to provide artificial intelligence, machine learning, and cloud services to the sports car manufacturer. The services provided by AWS will be used to test cars with an addition of a new fan engagement platform to Ferrari’s smartphone app.
The company is also planning to launch an augmented reality (AR) platform named Ferrari Garage, where the users will be able to experience the company’s cars in a virtual world. The company also plans to utilize these services for its vehicle information hub, where the company stores its car information and maintenance records.
The logo of AWS also appeared on Ferrari’s Formula 1 team at the French Grand Prix event, which took place on 20th June. Amazon Elastic Compute Cloud (Amazon EC2) will enable the carmaker to test complex simulations of cars in multiple driving, racing, and environmental conditions. This will help the company generate test reports much faster than the time taken in traditional on-premise testing.
Ferrari also plans to use Amazon ElasticKubernetes to improve the company’s car configurator, which is widely used to build customized cars in 2D and 3D formats. In addition, Scuderia Ferrari will utilize AWS services to develop a new digital platform where users will be able to connect to their favorite racers.
AWS has been the most popular cloud platform for over 15 years and has continuously worked to bring new advancements in their technology. Ferrari officials said they had chosen AWS because of its constant drive for innovation and the wide range of solutions they provide for machine learning and artificial intelligence.
“Ferrari and AWS are both exceptional in their respective spheres of activity, and I am pleased to welcome a partner known for the excellence of its innovation and creativity,” said the Managing Director of Ferrari, Mattia Binotto.
Fiddler Labs announced it had raised $32 million in its second round of funding. The fund will be used to provide access to the company’s Model Performance Management platform powered by Explainable AI to enable the team to develop Responsible AI in production.
Private equity firm Insight Partners, Amazon, and Global ventures led the funding round along with existing investors like Haystack Ventures, Lockheed Martin, The Alexa Fund, and Bloomberg Beta. As artificial intelligence powered decision-making has drastically expanded into every sector, a growing demand has been seen for the processes, tools, and understanding needed to deploy machine learning models responsibly.
Fiddler said that it aims to build trust in artificial intelligence as modern platforms are so complex that they resemble ‘black holes.’ George Mathew, Managing director of Insight Partners, said that he believes every company in the near future has to adopt AI. He added, “Through its unique MPM platform, Fiddler accelerates the march to an AI-first future while managing the ever present challenges of Explainability & Bias Detection.”
The company’s Explainable AI and ML Monitoring Platform is now setting a benchmark for machine learning engineers and data scientists as they implement their AI initiatives. Fiddler was founded in 2018 by Krishna Gade, who earlier worked with Facebook, where he led a team that developed explainability tools for the machine learning models behind Facebook’s Newsfeed.
The enterprise acquired a spot in Forbes top 50 AI company list in 2021 and was named a World Economic Forum Technology Pioneer in 2020. The current CEO of the company, Gade, said that they have already expanded enough to encompass every stage of the artificial intelligence lifecycle, from development to production, after the launch of the company’s artificial intelligence platform.
He also added that with the new funding, the company would continue to enhance their Model Performance Management solution, help resolve issues like data drift and bias, and educate people about ‘Responsible artificial intelligence.’
Coursera Global Skill Report is based on performance data of more than 70 million learners from across 100 countries on the platform collected since the onset of the pandemic. It benchmarks skills proficiency across categories like business, technology and data science.
Asia Pacific Region
Asia Pacific countries that have invested in national artificial iIntelligence programs have performed well. These countries include China who aims to develop a domestic AI industry worth $150 billion in the next few years, and Singapore whose national AI strategy aims to invest $150 million over five years.
In the field of data Science, Japan leads the region of Asia Pasific. Data skills present a unique opportunity for Japan, which has a national healthcare system that is a treasure chest of data. Demographic conditions and rising healthcare costs also support the business case for further investments in the digital transformation of healthcare.
Central Asia including India, which ranked 66th, performed the worst in data science skills in the Asia Pacific region. Governments around the region have started to earmark budgets to address these gaps. Kazakhstan is investing in the implementation of the “Digital Silk Way” to improve its high-speed and security infrastructure for the transfer, storage, and processing of data.
North America
The west coast of America remains the leader in data science, but the northeast and midwest are catching up. Many top Universities in these regions like the Massachusetts Institute of Technology (MIT), and Carnegie Mellon University (CMU) have invested recently in new data science degree programs.
Learners in the South of the country score particularly low in mathematics. Eighty-two percent of southern states are below the national average for eighth-grade math on standardized tests
Canada is lagging in probability & statistics. Once a top-10 country in the world in mathematics education according to PISA, has now had its ranking and math scores decrease consistently for the past 15 years.
Europe is a global leader in machine learning. Finland has been leading the way in AI education with a recent push by its government and the University of Helsinki to teach 1% of the world basic AI skills
Northern and western Europe lead the world in data science proficiency. Eight out of the top 10 countries across the globe in data science are in this region. The data economy’s value in the EU27 is predicted to increase to over €550 billion by 2025, representing 4% of the overall EU GDP.
Few countries stand out globally in one or two skills, despite lagging overall relative to european neighbours. Azerbaijan shows strengths in data analysis and mathematics, Slovakia, Lithuania, and Serbia excel in machine learning and a number of complementary data science skills.
Latin America And Caribbean Region
Latin American countries are top-performers in data analysis, and statistical programming. This is mainly of the “nearshore” outsourcing trend, which has allowed US-based companies to outsource analytical programming work to Latin America.
Venezuela, Uruguay, Argentina, and Costa Rica score the highest in data science skills in the region as these countries have high concentrations of “tecnolatinas” (tech startups) that have emerged in the region. Tecnolatinas include companies like MercadoLibre, Despegar, and Globant.
Mathematics skills are lacking across the region. These results align with other studies like the OECD’s PISA assessment, where the average math score for Latin American students was a Level 1, the lowest possible out of six.
Middle East and North Africa
Data visualization is an under-tapped strength for the region, particularly in Northern Africa. Egypt, Morocco, and Algeria are all globally competitive in these skills. The global data visualization market was valued at $2.99 billion in 2020 and expected to reach $5.17 billion by 2026.
Data science and statistical programming are lagging for the region. These skills are associated with some of the fastest growing jobs in the region between 2018 and 2022—data scientists and data analysts.
Israel on the contrary continues investing to become a global artificial intelligence leader. In 2020, Israel ranked behind only the US, China, and the UK in terms of private investment in artificial intelligence. It’s also a global leader in AI technologies applications in industries like education and manufacturing.
Sub-Saharan Africa
African countries are global leaders in data visualization. These skills are particularly important to local, open-source community initiatives such as Code for Africa, which focuses on data journalism as a key tool to promote digital democracies and empower citizens with actionable information.
With the exception of South Africa and Rwanda, most African countries perform poorly in mathematics. Nearly nine out of ten children between the ages of about 6 and 14 in sub-saharan Africa will not meet minimum proficiency levels in reading and math.
Nigeria lags behind other large countries. Despite being Africa’s largest economy and having one of the most vibrant startup hubs in Lagos, Nigeria ranks near the bottom globally in data skills
Conclusion
Coursera’s Global Skill Report shows evidence that the world is witnessing exponential growth in the data science and artificial intelligence industry. Data & AI jobs have been among the fastest-growing, at 40+% annualized growth over the past five years. Countries like the USA and entire Europe are investing massive amounts of money in this sector which has created thousands of jobs. The problem is the growth is concentrated in a few pockets around the globe. Countries should be encouraged to invest more in this industry by introducing AI and data science-related courses in the Universities and by supporting start-ups that deal in this domain to create a homogenized growth ecosystem worldwide.
KeepTruckin has recently raised $190 million in a Series E funding round, making its valuation at $2 billion. The company develops hardware and software that helps manage cargo vehicles and ensure driver safety.
Existing investors along with a few new firms like G2 Associates, participated in the funding round. According to the company’s CEO Shoaib Makani, KeepTruckin plans to invest this money to improve its AI-powered smart dashcam, which detects unsafe driving activities like mobile distraction and alerts the driver. Along with the dashcam, the company also intends to enhance its range of AI products like GPS tracking and ELD compliance.
Shoaib Makani said, “KeepTruckin’s specialty is that we can build complex models and make it run on the edge with low-power, low-memory, and low-bandwidth constraints.” He further added that they had developed in-house IPs to resolve problems at various environmental conditions like extreme weather, occluded subject, low light, and distortions.
Usher Transport, one of KeepTruckin’s clients, stated that they recorded an annual reduction of 32% in accidents after using the safety products offered by KeepTruckin, such as the Smart Dashcam.
The enterprise’s self-developed machine learning platform trains and tests billions of ground truth data points, which is a very resource-intensive process to deliver high accuracy outcomes.
According to Makani, the machine learning platform includes smart annotation capabilities to automatically label the different data points so the neural network can simulate innumerable potential situations, achieving similar performance to the edge device in the field with real-world environmental conditions.
Since the pandemic, KeepTruckin said it experienced 70% annual growth, mainly because of its expansion into new markets like food and beverage, field services, construction, oil and gas, and agriculture. The company expects the demand to rise and aims to use the recently acquired funds to recruit more talent and expand its R&D team to 700 employees globally.
On Tuesday, tech giant Oracle predicted its current-quarter revenue under Wall Road estimates, as the company’s software developers ramp up investments in its cloud computing enterprise to compete against rivals together with Amazon and Microsoft.
Oracle plans to double its capital spending on the cloud segment to around $4 billion in fiscal 2022 as they see higher profit because of the pandemic with companies choosing hybrid-work culture.
Increased investment in the cloud segment has led Oracle to forecast its Q1 earnings per share of 94 cents to 98 cents, under expectations of $1.03, sending its shares down 4.5% in prolonged buying and selling.
The company has been organizing extra information facilities to aid corporates as they widen their operations. They intend to enhance their cloud platform to catch high-profit clients just the way Zoom Communications did in the recent past.
This 45 years old company is seen as a distinct segment participant by analysts and trade experts when compared to other giants like Google, Amazon, and Microsoft.
An analyst at Third Bridge, Scott Kressler, said, “The company, nonetheless has plenty of work to do and progress to make earlier than they’re thought-about in the identical class because the main cloud infrastructure corporations.” He also mentions, “One among Oracle’s greatest points is how small the corporate’s income development has been, regardless of a concentrate on its cloud options.”
The firm said it expects its current-quarter income at its largest unit Cloud companies and license help to increase by 4%, which involves around US$7.2 billion, according to expert’s calculations. Analysts commonly count on it to be $7.36 billion.
Oracle posted revised revenue of $1.54 per share on income of $11.23 billion within the fourth quarter ended Could 31, in contrast with estimates of revenue of $1.31 billion and income of $11.04 billion.
An artificial intelligence-based start-up, Vianani System has raised $140 million in their second funding from SoftBank Vision and trade luminaries. The company announced this development on Wednesday 16 June.
Vikas Sikka founded Vianani after parting ways with the IT giant Infosys in 2017. The company said that it would utilize the funds to ramp up the development speed of its AI platform and related products to offer quick supply the enterprise clients.
Vikas Sikka, in an interview, said, “We have been working hard to build a better AI platform, one that puts human judgment at the center of systems that bring vast AI capabilities to amplify human potential.” He added that he is immensely grateful for the trust that the customers and investors have put in them.
Vianai mentioned that they are developing a Human-centric AI platform that would amplify human judgment skills, which would empower area specialists with AI instruments to ship enterprise worth. The company in the past has proven itself by delivering profitable outcomes to many industry-leading companies, in addition.
The company announced that Fei Fei Lee, Co-Director of the Stanford Institute for Human-Centered AI, will be one of the members of the advisory board. Deep Nishar, a Senior Managing Partner at SoftBank Investment Advisers, said, “With the AI revolution underway, we believe Vianai’s human-centered AI platform and products provide global enterprises with operational and customer intelligence to make better business decisions.”
Viani also mentioned that along with SoftBank, its buyers include Yahoo co-founder Jerry Yang, KKr co-founds George Roberts and Henry Kravis, and Silver Lake co-founder Jim Davidson. Earlier in 2019, the company raised US$50 million in its first round of funding.
