Home Featured Me, Myself and AI is the rear view mirror

Me, Myself and AI is the rear view mirror

Me, Myself and AI is the  rear view mirror

I wanted to meet Sophia in London a few weeks ago. Sophia is a popular personality like Audrey Hepburn. At that time, Sophia was also a machine. What makes it interesting can bring a conversation.

She hears what you use, shows her facial expressions, answers your questions, and asks her questions.

Sophia is one of the few examples of machine intelligence that has come in recent years. Although the use of robots as the main user interface is still limited, the actual use of artificial intelligence (AI) in image processing, voice recognition and natural language processing is now commonplace.

Names for Sophia and other AI demonstrations were discovered in the 1940s and 1950s, early cybernetics, computing and artificial neural networks, and through the development of learning machine algorithms.

Capture man.

As the field grows and over the past few decades, everything has ended. For example, it is assumed that winning the human master in games like Go will exceed AI capacity, whereas the winner’s strategy can not be found with abusive style computing.

It turns out, AlphaGo (created by DeepMind, owned by Google) beat Lee Sedol 4-1 world champion in a series of five games two years ago, when displaying human features such as intuition.

Rapid progress has been made on AI for some reason.

There are many great computing tools like cloud computing and fast supercomputers, along with important theoretical advancements in machine learning algorithms, which means we can do anything that’s impossible before.

However, practical and practical system training can take hours, days, or weeks, depending on what you are doing. However, current AI applications can not be made.

Grist for AI refineries.

But the AI ‚Äč‚Äčtraining algorithm does not merely calculate the power. Having relevant data is the key to making further progress.

Most AIs include a learning machine where automated methods are used to find patterns in large data sets, to override objects, and to predict what will happen next. In some tasks, the machine – after many examples and many, is, the data – better than anyone can hope.

Fortunately, we live in an era where data on various types and amounts are now available. Notwithstanding, smartphones, connected devices, home or garden robots.

and a large number of sensors around us, means a large amount of information gathered about humanity, from our location, health profile, location and demographics, for our financial transactions and interactions with others.

However, many (if not all) data are original. Personal Aspects are necessary to improve privacy and confidence issues.

My name is me.

Is my privacy respected, or personal data collected without my password? Who made the collection and how? Is your personal data safe? Does the data retain its own personal intellectual property? Is raw data, or data knowledge, available to authorities and governments, either myself or others?

Events like Cambridge Analytica who allegedly launched Facebook data in this way have brought the problem to open. Once again, new stories like Amazon recorded personal conversations and sent to co-workers with a blur.

As soon as we start working on many devices in our home, all listen to the sound and even give directions, there is a potential confusion and deeper concern issues, such as starting a machine in the middle of themselves and seeking commercial transactions with each other.

In addition, what is the incentive for ordinary people to share personal data? In some cases, I also want to share information without compensation if it benefits the community or the general truth. I’m also very excited to spend data when I get access to new services, or if there’s a better service with more data.

Show that treatment?

This is the conceptual that has happened to users, Google Maps. Your phone and other connected devices track geolocation, speed, and title.

When information is collected and sent back to the tracking algorithm, the better picture of real-time traffic flow is better.

Members share data for free but receive better functionality. Google, of course, makes huge profits from showing ads for the same users and knows more about them and their capabilities can be used.


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