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Last week, we published a guest blog by our dear Vibhav Mithal, which describes a ‘Trust and Ethical AI.’
What began as a discussion on the topic “Business Rational for Ethical AI” prompted our Head of Community to wonder what the business justification could be for developing ethical AI systems. We all know that AI is everywhere – just think of Alexa, Netflix, YouTube, Spotify, and any other search engine you may use. It could be a part of our professional lives, homes, cities, and so on. I mean, we’ll be surrounded by more than 125 billion IoT devices by 2030. As for 5G IoT devices, 5G connections will increase to 3.3 billion in 2030, with no remaining 3G connections in the market and just 120 million 2G connections by 2030.
These devices collect a lot of personal information in terms of optimizing the process. This use of data has a specific monetary value. That data collected is then used to train the AI, and the data contains patterns that the AI can recognize. Understanding the data on which it is built is critical if we are looking for ethical AI or developing ethical AI – this builds trust.
Nevertheless, this is not the end of the story. Last week’s BBC Panoramic program “Are you scared yet, human?” demonstrated how artificial intelligence (AI) is transforming our world. Following that, top Silicon Valley executives confirmed their concerns about the future. Alan D. Mutter, a consultant and former CEO of Silicon Valley, wrote, “We will not be able to climb out of the moral and political quagmire that we have created. Technology will help if the right people do the right thing. If they don’t, epic damage will befall them.” Even Microsoft President Brad Smith believes that George Orwell’s 1984 could come true by 2024.
It makes us wonder: if humans have struggled to create truly intelligent machines, perhaps we should let them do it independently. According to a study that encourages AI to learn how to create itself, this brings us one step closer to achieving that goal.
Machines now possess or have access to vast amounts of knowledge. The human brain cannot store or process nearly as much data or information as a computer. That is perfectly acceptable. The issue is that most training today still focuses on knowledge transfer. However, knowledge is not the main capacity we need; instead, it develops skills in various fields.
Hottest topics from the community
This week, the MKAI community discussed more philosophical questions. One of the triggers for this conversation line was AI’s ability to contact/connect with souls. Another studious tea session in the afternoon yielded a slew of brilliant responses. The MKAI community pondered whether awareness exists only in the brain or if it can be explained scientifically. According to Paul Levy’s words, it is still very mysterious to us regarding its origin. Not just its effect, which we often confuse with its origin, the spiritual metaphor might give clues as to how AI could develop to become genuinely complex in its behavior, not just its complex machine calculations and code; because AI is impossible without mindfulness.
It is possible to argue that consciousness is more than just the ability to feel negative emotions. Indeed, people have long believed that consciousness has some advantages. We expected it to make us more ethical, moral or provide us with an understanding of the ultimate meaning of the universe. Despite this, there have been significant milestones of artificial intelligence in our history.
Besides artificial intelligence, it’s obvious that we still seek to understand the origins of our intelligence, human intelligence. As we haven’t found answers on our planet, at one point, humanity decided to search for extraterrestrial intelligence, in order to understand us better. While doing that, we are confronted with ethical issues concerning space research – whether we’re doing it for a higher purpose or other reasons. However, we don’t know if these searches will be successful. We have no choice but to look and see what we find because it would have far-reaching consequences.
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Open-source knowledge exchange
At the MKAI June Technical Forum on Machine Learning and AI, we will learn about the latest Machine Learning Methodologies directly from industry practitioners. Soft skills make you more likely to be a successful engineer than hard skills. In our opinion, a project’s success and delivery, however, depend primarily on soft skills such as communication, problem-solving, managing time, and collaborating with others.
For machine learning engineers to know both the business’s needs and the types of problems their designs are solving, they must have a firm understanding of both. Because machine learning engineers are domain-limited, their recommendations may be inaccurate.
Opening Technical Presentation will be led by Giovanna Reggina Galleno Malaga and Ghaith Sankari. Speakers at the second technical presentation will be Ammar Mohanna and Manpreet Budhraja. We’ll be discussing the topic of ‘Soft Skills’ Development for Machine Learning Production Teams.
REGISTER HERE https://mkai.org/event/june-technical-forum/
Community success
We learned a lot at the most recent MKAI Inclusive Forum, titled Rethinking Data Monetization. Tara McKeown and Vibhav Mithal from our community were among the other panelists.
We’ve learned that there’s no time to wait for governments and organizations to put in place a more comprehensive, ethical framework, around data monetization. Before we can talk about regulation and changing consumer habits, we need to talk about changing the architectural infrastructure. Then we’ll be one step closer to better legislation. Until that happens, people are human beings regardless of what is going on around them (laws, GDPR).
The introduction of ‘Information ethics’ in the twenty-first century concerned various permissions and obligations that may apply to disseminating and using information. We could have a variety of legal obligations and permissions (e.g., under the GDPR).
Indeed, information philosophy has only recently emerged – in this context, MKAI aims to develop a pragmatic understanding of related issues in artificial intelligence philosophy, involving a rethink of data monetization. The data monetization economic model must be rethought. This is how we can make it equitable. Data monetization should be discussed from the perspective of the data subject. This is not about determining or regulating a value; rather, it is about the data subject deciding where to spend their data. We concluded that there is no time to wait for regulation or a shift in consumer habits – architectural infrastructure must be altered.
Just one more thought experiment: how do our beliefs and knowledge, both individually and collectively, influence this? And related issues arise in a variety of ways in the digital sphere.
If you have an answer to this, feel free to reach out. Also, if any of the topics left a particular impression or resonated with you – feel free to join our focused discussion group on Telegram.
See you next week,
A.
