Artificial Intelligence and Machine Learning
Artificial Intelligence and Machine Learning
Artificial Intelligence:
Artificial intelligence is a technology that is already impacting how users interact with, and are affected by the Internet. In the near future, its impact is likely to only continue to grow. AI has the potential to vastly change the way that humans interact, not only with the digital world, but also with each other, through their work and through other socioeconomic institutions – for better or for worse.
If we are to ensure that the impact of artificial intelligence will be positive, it will be essential that all stakeholders participate in the debates surrounding AI.
In this paper, we seek to provide an introduction to AI to policymakers and other stakeholders in the wider Internet ecosystem.
Introduction
Artificial intelligence (AI) has received increased attention in recent years. Innovation, made possible through the Internet, has brought AI closer to our everyday lives. These advances, alongside interest in the technology’s potential socio-economic and ethical impacts, brings AI to the forefront of many contemporary debates. Industry investments in AI are rapidly increasing and governments are trying to understand what the technology could mean for their citizens.
The collection of “Big Data” and the expansion of the Internet of Things (IoT), has made a perfect environment for new AI applications and services to grow. Applications based on AI are already visible in healthcare diagnostics, targeted treatment, transportation, public safety, service robots, education and entertainment, but will be applied in more fields in the coming years. Together with the Internet, AI changes the way we experience the world and has the potential to be a new engine for economic growth.
The Internet Society recognizes that understanding the opportunities and challenges associated with AI is critical to developing an Internet that people trust. This is particularly important as the Internet is key for the technology behind AI and is the main platform for its deployment; including significant new means of interacting with the network. This policy paper offers a look at the key things to think about when it comes to AI, including a set of guiding principles and recommendations to help make sound policy decisions. Of particular focus is machine learning, a specific approach to AI and the driving force behind recent developments.
Artificial Intelligence and what is it all about
Artificial intelligence (AI) traditionally refers to an artificial creation of human-like intelligence that can learn, reason, plan, perceive, or process natural language.
Artificial intelligence is further defined as “narrow AI” or “general AI”. Narrow AI, which we interact with today, is designed to perform specific tasks within a domain (e.g. language translation). General AI is hypothetical and not domain specific, but can learn and perform tasks anywhere. This is outside the scope of this paper. This paper focuses on advances in narrow AI, particularly on the development of new algorithms and models in a field of computer science referred to as machine learning.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
But, using the classic algorithms of machine learning, text is considered as a sequence of keywords; instead, an approach based on semantic analysis mimics the human ability to understand the meaning of a text.
Algorithms are a sequence of instructions used to solve a problem. Algorithms, developed by programmers to instruct computers in new tasks, are the building blocks of the advanced digital world we see today. Computer algorithms organize enormous amounts of data into information and services, based on certain instructions and rules. It’s an important concept to understand, because in machine learning, learning algorithms – not computer programmers – create the rules.
The basic process of machine learning is to give training data to a learning algorithm. The learning algorithm then generates a new set of rules, based on inferences from the data. This is in essence generating a new algorithm, formally referred to as the machine learning model. By using different training data, the same learning algorithm could be used to generate different models. For example, the same type of learning algorithm could be used to teach the computer how to translate languages or predict the stock market.
Inferring new instructions from data is the core strength of machine learning. It also highlights the critical role of data: the more data available to train the algorithm, the more it learns. In fact, many recent advances in AI have not been due to radical innovations in learning algorithms, but rather by the enormous amount of data enabled by the Internet.
Why now?
Machine learning is not new. Many of the learning algorithms that spurred new interest in the field, such as neural networks, are based on decades old research. The current growth in AI and machine learning is tied to developments in three important areas:
- Data availability: Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
- Computing power: Powerful computers and the ability to connect remote processing power through the Internet make it possible for machine-learning techniques that process enormous amounts of data.
- Algorithmic innovation: New machine learning techniques, specifically in layered neural networks – also known as “deep learning” – have inspired new services, but is also spurring investments and research in other parts of the field.
- Decision-making: transparency and “interpretability”. With artificial intelligence performing tasks ranging from self-driving cars to managing insurance payouts, it’s critical we understand decisions made by an AI agent. But transparency around algorithmic decisions is sometimes limited by things like corporate or state secrecy or technical literacy. Machine learning further complicates this since the internal decision logic of the model is not always understandable, even for the programmer.
- Data Quality and Bias. In machine learning, the model’s algorithm will only be as good as the data it trains on – commonly described as “garbage in, garbage out”. This means biased data will result in biased decisions. For example, algorithms performing “risk assessments” are in use by some legal jurisdictions in the United States to determine an offenders risk of committing a crime in the future. If these algorithms are trained on racially biased data, they may assign greater risk to individuals of a certain race over others. Reliable data is critical, but greater demand for training data encourages data collection. This, combined with AI’s ability to identify new patterns or re-identify anonymized information, may pose a risk to users’ fundamental rights as it makes it possible for new types of advanced profiling, possibly discriminating against particular individuals or groups.
- Safety and Security. As the AI agent learns and interacts with its environment, there are many challenges related to its safe deployment. They can stem from unpredictable and harmful behavior, including indifference to the impact of its actions. One example is the risk of “reward hacking” where the AI agent finds a way of doing something that might make it easier to reach the goal, but does not correspond with the designer’s intent, such as a cleaning robot sweeping dirt under a carpet.
- Accountability. The strength and efficiency of learning algorithms is based on their ability to generate rules without step-by–step instructions. While the technique has proved efficient in accomplishing complex tasks such as facerecognition or interpreting natural language, it is also one of the sources of concern.
- Governance. The institutions, processes and organizations involved in the governance of AI are still in the early stages. To a great extent, the ecosystem overlaps with subjects related to Internet governance and policy. Privacy and data laws are one example.
All the world’s tech giants from Alibaba to Amazon are in a race to become the world’s leaders in artificial intelligence (AI). These companies are AI trailblazers and embrace AI to provide next-level products and services.
The artificial intelligence industry is expected to reach $59.8 billion by 2025. With use cases in almost every industry vertical, artificial intelligence is predicted to be the future of technology by thought leaders including Bill Gates. From sales forecasting to improving productivity, the application of artificial intelligence (AI) is immense for companies worldwide.
Here are the insights to some top companies that have the power and resources to shape our connected future. These are the big players in artificial intelligence
1.Amazon
Not only is Amazon in the artificial intelligence game with its digital voice assistant, Alexa, but artificial intelligence is also part of many aspects of its business. Another innovative way Amazon uses artificial intelligence is to ship things to you before you even think about buying it. They collect a lot of data about each person’s buying habits and have such confidence in how the data they collect helps them recommend items to its customers and now predict what they need even before they need it by using predictive analytics.In a time when many brick-and-mortar stores are struggling to figure out how to stay relevant, America’s largest e-tailer offers a new convenience store concept called Amazon GO. Unlike other stores, there is no checkout required. The stores have artificial intelligence technology that tracks what items you pick up and then automatically charges you for those items through the Amazon Go app on your phone. Since there is no checkout, you bring your own bags to fill up with items, and there are cameras watching your every move to identify every item you put in your bag to ultimately charge you for it.
In a time when many brick-and-mortar stores are struggling to figure out how to stay relevant, America’s largest e-tailer offers a new convenience store concept called Amazon Go. Unlike other stores, there is no checkout required. The stores have artificial intelligence technology that tracks what items you pick up and then automatically charges you for those items through the Amazon Go app on your phone. Since there is no checkout, you bring your own bags to fill up with items, and there are cameras watching your every move to identify every item you put in your bag to ultimately charge you for it.
2.Facebook
With over 3 billion users, worldwide, Facebook is the leading social networking site in the world. One of the primary ways Facebook uses artificial intelligence is to add structure to its unstructured data. They use DeepText, a text understanding engine, to automatically understand and interpret the content and emotional sentiment of the thousands of posts (in multiple languages) that its users publish every second. With DeepFace, the social media giant can automatically identify you in a photo that is shared on their platform. In fact, this technology is so good, it’s better at facial recognition than humans.
Their internal group called Facebook AI Research(FAIR) is committed to solving challenges in AI. Apart from acquiring AI companies like Masquerade and Zurich Eye, the company has also invested strategically in their own artificial intelligence labs. The company’s AI research team led by deep learning pioneer, Yann LeCun has many major initiatives planned for 2018 to improve the efficiency of the social media platform.
3.Google
Perhaps the largest and most important AI company among all is also the most obvious. Google has acquired AI start-ups as if there were going to be no more soon. Over the past four years, Mountain View has created no fewer than twelve new artificial intelligence companies. The most important purchase was the $400 million deal for DeepMind, the board game playing Go champion.
There is also Google’s machine system TensorFlow, which is now free for all, and the ongoing Tensor AI chip project for machine learning on the device. Google’s CEO, Sundar Pichai, has already mentioned that in the long run we are “evolving from a ‘mobile first’ to an ‘AI-first’ world in the computer industry,” and that already says everything you need to know to see where Google sees the future.
4.Microsoft
AI is a term that appears on Microsoft’s vision statement, which illustrates the company’s focus on having smart machine central to everything they do. They are one of the world’s biggest AI as a Service (AIaaS) vendors.
As one of the leading software companies, Microsoft has been building its AI capabilities on different fronts to drive their business. With a variety of AI-based products and services like Cortona, CNTK, cognitive services, and industry-specific AI apps, Microsoft offers developers many interesting and challenging projects in AI.
5.Adobe
More than 30 per cent of the 400 patents Adobe filed last year were specifically related to artificial intelligence and machine learning.
Adobe introduced its AI capability Sensei four years ago and has since launched hundreds of new AI-powered features and capabilities across its portfolio. Within its marketing, advertising and commerce clouds, the AI services aim to help marketers to optimise campaigns, automate tedious tasks and provide predictive capabilities. Adobe has several new programs and projects focused on building better tools powered by AI. The company plans to incorporate more AI-based technology in its services and products.
6.Apple
Apple has been busy acquiring AI start-ups in recent years and sees Artificial Intelligence as a critical part of its future. In December 2018, the company officially appointed John Giannandrea as head of the AI and Machine Learning department after Google poached the Scottish computer scientist. He will oversee the development of products such as Siri and the company’s new Create ML tool, which MacOS and iOS developers can use to create efficient and straightforward training courses for their apps.
7.Intel
Intel has also been on a shopping spree when it comes to artificial intelligence companies and has acquired both Nervana and Movidius as well as a selection of smaller AI start-ups. Nervana enables companies to develop specific deep learning software, while Movidius was founded to bring AI applications to devices with deficient performance. Intel is also working with Microsoft to provide AI acceleration for the Bing search engine.
8.Nvidia
Nvidia is one of the longest established AI companies and still plays an important role today. Nvidia’s graphics processors are the be-all and end-all for machine learning and artificial intelligence. The Delaware-based company is active in healthcare, higher education, retail, and robotics. With deep learning and GPU development, Nvidia is concerned with integrating AI into every level of the vehicle, manufacturing and autonomous driving.
9.Qualcomm
Like HiSilicon with its Kirin 980, Qualcomm is another chip manufacturer that is committed to artificial intelligence. AI plays a crucial role in the Snapdragon 855 mobile platform. The chip uses a signal processor for AI speech, audio and image functions. Qualcomm Snapdragons power some of the most popular smartphones on the market. If you’re interested in AI in the smartphone, you should keep an eye on Qualcomm.
10.Twitter
Like the other big players in Silicon Valley, Twitter is all about getting into artificial intelligence, especially with money. Four AI companies have already been acquired, with Magic Pony being the most notable for $150 million. The Australian company is developing machine learning approaches for visual processing on the web and mobile devices, and Twitter is likely to improve its systems for recommending specific tweets in users’ timelines with AI in the future.
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