AI とは?歴史、定義、応用
誰もが人工知能について話しているが、その略称である AI としても知られている。しかし、それは一体何なのでしょうか?それがまさに今日説明する内容です。
- AI は個人の責任の終わりを意味しますか?
- スマートフォンの AI の本当の意味
歴史
人工知能は、私たちの生活の中でますます大きな役割を果たしています。最新のトレンドは、AI チップとそれに付随するスマートフォン アプリケーションです。しかし、この技術は 1950 年代に、米国のダートマス カレッジで行われた人工知能に関するダートマス サマー リサーチ プロジェクトによって開発され始めました。 —、アレン・ニューウェルとハーバート・A・サイモン、しかしAIは世界の舞台で脚光を浴びることはありませんでしたが、IBMによってチェスのスーパーコンピューターであるディープ・ブルーが登場しました。ディープ・ブルーは当時のディフェンディング・ワールド・チェス・チャンピオンのギャリー・カスパロフを破った最初のマシンでした。 1996 年の試合で。AI アルゴリズムは、データ センターや大型コンピューターで長年使用されてきましたが、消費者向け電子機器の領域に登場したのはごく最近のことです。
人工知能の定義
人工知能の定義では、インテリジェントな動作の自動化を扱うコンピューター サイエンスの一分野として特徴付けられています。ここが難しい部分です:知性を正確に定義することはそれ自体できないため 、人工知能も正確に定義することはできません。一般的に言えば、この用語は、機械を使用して人間の知性とそれに対応する動作をエミュレートおよびシミュレートすることを目的とするシステムを表すために使用されます。これは単純なアルゴリズムと定義済みのパターンで実現できますが、はるかに複雑になることもあります。
さまざまな種類の AI
シンボリックまたはシンボル操作 AI 知識を表すために使用される抽象的な記号で動作します。人間の思考を階層的かつ論理的なレベルで再構築できるという考えを追求するのは、古典的な AI です。情報は上から処理され、人間が読める記号、抽象的な接続、論理的な結論を処理します。
- AI はビデオ ゲームで育てられています...私たちを打ち負かすためではなく、私たちに加わるために
ニューラル AI 80 年代後半にコンピューター サイエンスで人気を博しました。ここでは、知識は記号ではなく、人工ニューロンとその接続によって表されます。つまり、再構築された脳のようなものです。収集された知識は小さな断片 (ニューロン) に分解され、結合されてグループに組み込まれます。 This approach is known as the bottom-up method that works its way from below. Unlike symbolic AI, a neural system must be trained and stimulated so that the neural networks can gather experience and grow, therefore accumulating greater knowledge.
Neural networks are organized into layers that are connected to each other via simulated lines. The uppermost layer is the input layer, which works like a sensor that accepts the information to be processed and passes it on below. This is now followed by at least two—or more than twenty in large systems—layers that are hierarchically above each other and that send and classify information via the connections. At the very bottom is the output layer, which generally has the least number of artificial neurons. It provides the calculated data in a machine-readable form, i.e. "picture of a dog during the day with a red car."
Methods and tools
There are various tools and methods for applying artificial intelligence to real-world scenarios, some of which can be used in parallel.
The foundation of all this is machine learning , which is defined as a system that builds up knowledge from experience. This process gives the system the ability to detect patterns and laws—and with ever-increasing speed and accuracy. In machine learning, both symbolic and neural AI is used.
Deep learning is a subtype of machine learning that is becoming ever more important. Only neural AI, i.e. neural networks are used in this case. Deep learning is the foundation for most current AI applications. Thanks to the possibility of increasingly expanding the design of the neural networks and making them more complex and powerful with new layers, deep learning is easily scalable and adaptable to many applications.
There are three learning processes for training neural networks:supervised , non-supervised and reinforcement learning , providing many different ways to regulate how an input becomes the desired output. While target values and parameters are specified from the outside in supervised learning, in unsupervised learning, the system attempts to identify patterns in the input that have an identifiable structure and can be reproduced. In reinforcement learning, the machine also works independently, but is rewarded or punished depending on the success or failure.
Applications
Artificial intelligence is already being used in many areas, but by no means are all of them visible at first glance. Therefore, selecting scenarios that take advantage of the possibilities of this technology is by no means a completed list.
Artificial intelligence’s mechanisms are excellent for detecting, identifying, and classifying objects and persons on pictures and videos. To that end, simple but CPU-intensive pattern detection is used. If the image information is decrypted and machine-readable in the first place, photos and videos can be easily divided into categories, searched and found. Such recognition is also possible for audio data.
Customer service is increasingly using chatbots . These text-based assistants perform recognition using key words that the customer may tell it and they respond accordingly. Depending on the use, this assistant can be more or less complex.
Opinion analysis is not only used for forecasting elections in politics, but also in marketing and many other areas. Opinion mining, also known as sentiment analysis, is used to scour the internet for opinion and emotional expressions, allowing for the creation of a largely anonymized opinion survey.
Search algorithms like Google’s are naturally top secret. The way in which search results are calculated, measured and outputted are largely determined by mechanisms that work with machine learning.
Word processing , or checking the grammar and spelling of a text, is a classic application of symbolic AI that has been used for a long time. Language is defined as a complex network of rules and instructions that analyzes blocks of text in a sentence and, under some circumstances, can identify and correct errors.
These abilities are also used in synthesizing speech , which is currently the talk of the town with assistant systems like Siri, Cortana, Alexa or Google Assistant.
On new smartphone chips like the Kirin 970, artificial intelligence is integrated into its own component, the NPU or neural processing unit .The processor is making its debut in the Huawei Mate 10. You will learn more about it and the roles that the technology will play on the Huawei smartphone once we have a chance to experiment with it in the near future. Qualcomm has already been working on an NPU, the Zeroth processor, for two years, and the new Apple A11 chip contains a similar component.
Furthermore, there are numerous research projects on artificial intelligence and the most prominent of all may be IBM’s Watson. The computer program had already made its first public debut in 2011 on the quiz show Jeopardy, where it faced off against two human candidates. Watson won, of course, and additional publicity appearances took place afterwards. A Japanese insurance company has been using Watson since January to check insured customers, their history and medical data and to evaluate injuries and illnesses. According to the company’s information, Watson has replaced roughly 30 employees. Loss of jobs through automation is just one of the ethical and social issues surrounding AI that is the subject of corporate and academic research.
Projection
AI isn’t something that just came out of nowhere recently, but it is coming close to a breakthrough in the world of consumer electronics, which is more than enough reason for everyone to keep up to date with this topic in the future.
Which aspects of artificial intelligence do you find exceptionally interesting? Let us know in the comments below!