Rethinking Data Monetization
Data collection and analysis with machine learning algorithms is often an expensive, time-consuming, and complex process. To overcome these challenges, many organisations generate synthetic data. Synthetic data eliminates the need for businesses to collect, store, and label “real” data in a legally and morally compliant manner and can create significant cost savings. Beyond the commercial implications, there are many applications for synthetic data.
For example, synthetic data has the potential to help machines understand how emotions and subtleties differ from person to person. As time goes on, we’re likely to see more and more human-to-machine communication, and it will be vidtal that machines can delve deeper into our thinking than just the commands we press and the words we use.
Furthermore, having access to synthetic data will likely make it easier for smaller businesses to enter the AI space. These smaller businesses may have the foresight and ingenuity to develop truly novel applications, as Facebook did with its use of synthetic data. By using synthetic data companies need not be hampered by high production and compliance costs and have a far more equal opportunity to enter the data processing market.
So far, there is not a formal legal framework to guide us in our efforts to comply with AI law, but Europe has been moving in this direction for some time, with the EU Regulations were recently published on April 21, 2021. As a result, it is critical to consider legal compliance with AI on a global scale. Various entities are working to explain these regulations and discover ways to comply with them, and develop an essential legal framework to facilitate compliance.
Besides the EU, we have to focus on the United States and China, and their perspectives on the future of artificial intelligence play critical roles in AI regulation. Decisions impact the level of ethical participation and overall purpose, as well as the state of the economies and societies in which they are made. Because of the various options, we must plan for three possible scenarios for each civilisation (EU, USA, China). You can read more about them here.
Further calls to action – international regulation
These European civil society organizations have urged the European Commission to enact stricter biometrics and artificial intelligence regulations. The Reclaim Your Face campaign seeks to outlaw indiscriminate biometric surveillance on living citizens.
Following multiple instances of facial recognition errors that resulted in the wrong identification of Black suspects, there have been numerous calls for federal regulation of police use of facial recognition technology in the United States.
Furthermore, individuals working in biometric humanitarian applications, such as the United Nations High Commissioner for Refugees (UNHCR) and the World Food Programme (WFP), believe that emerging technologies for these settings will impact the entire identity industry and international ID systems.
Following this, international regulation may become even more critical.
Impact of technological sophistication
Returning to China again, in the near future China’s plans for artificial intelligence are pretty substantial, particularly concerning the goals of OpenAI and DeepMind. The Beijing Academy of Artificial Intelligence (BAAI), a non-profit research institute promoting collaboration between academia and industry, is committed to developing top talent and advancing long-term fundamental research on AI technology.
According to a city official, the academy received 340 million yuan (US$53.3 million) in Beijing government funding between 2018 and 2019 and has pledged to continue that support. The final figures in 2021 could be significantly higher.
Ready, set, 5G
More news from China. According to mainland industry leaders, approximately 5,000 private 5G networks have been installed, with tens of thousands more expected to be installed this year due to the ability of 5G broadband to support Fourth Industrial Revolution applications.
China already has 70 percent of the world’s installed 5G base stations and approximately 80 percent of the world’s 5G smartphone users.
According to Chinese industry sources, China will add 500,000 to 800,000 new 5G base stations to the 792,000 already in place by the end of February 2022.
The main difference between 5G and previous broadband technologies is not how fast it is, but how much data it can carry and how low the latency is (speed of response). With the arrival of 5G, which reduces response time by a factor of ten, previously unthinkable opportunities in industrial automation have emerged.
Everything is connected
While companies in the West are still determining whether to deploy private 5G networks, China is looking forward to the commercialisation of 6G around 2030. IMT-2030 (6G) Promotion Group’s white paper, issued in June 2019, claims that next-generation mobile communication technology will combine advanced computing, big data, artificial intelligence (AI), and blockchain.
Integrating the natural world and the virtual digital world using the 6G network is expected to result in a new world of “intelligent connection of everything and digital twins.”
As we mentioned in the previous weekly digest, 3.3 billion people will have 5G internet connections by 2030. Many of these devices collect a large amount of personal information by fine-tuning the process. This type of data application has a monetary value. Once the data has been collected, it is used to train the AI, and the patterns in the data aid in the training process. Understanding the data on which AI is built is critical for developing ethical AI because it builds trust.
However, we are frequently told that achieving trust and ethics in AI is complicated. It’s not simply a matter of making a public declaration and then putting in place the necessary policies and investments. So one of the ways businesses and organisations can help their customers as much as possible is to make their intentions, data and algorithmic decision making as transparent and understandable as possible.
By sharing the lessons learned with businesses’ AI ecosystem, there is hope to empower companies and organisations by guiding them in using insights that work to achieve concrete results. This, for example, could have a significant impact on the industry’s climate change commitments.
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Open-source knowledge exchange
We will learn about the latest Machine Learning Methodologies from industry practitioners at the MKAI June Technical Forum on Machine Learning and AI. Incorporating soft skills increases your chances of being a successful engineer. Creative: Soft skills such as communication, problem-solving, managing time, and collaborating with others, we believe, are crucial to project success and delivery.
To achieve an accurate comprehension of both business needs and types of machine learning design problems, machine learning engineers must have a thorough understanding of both. Inaccurate machine learning recommendations may be provided by domain-limited engineers.
Giovanna Reggina Galleno Malaga and Ghaith Sankari will open the technical presentation with their opening presentation. Ammar Mohanna and Manpreet Budhraja will be present at the second technical presentation. The topic of “Soft Skills” Development for Machine Learning Production Teams will be a topic for discussion.
Interested in artificial intelligence in 2030? Come and join us for the MKAI Inclusive AI Forum, an event designed to examine the way in which AI will be distributed, powerful, adopted, controlled, safe, and widespread over the next decade.
Please join us on June 24th at 5 p.m. (BST) as our guest. We want to know what you think. Please fill out the registration form.