If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
I recently had the privilege of participating on a panel with several AI/Machine Learning experts. There were many great questions, but most were related to how to most effectively establish an AI/Machine Learning (AI/ML) in a large organization. This gave me an opportunity to reflect on my own experiences helping large enterprise accelerate their AI/Machine Learning journey, and, more specifically, assess what worked, and perhaps just as importantly, what did not work. I have condensed these into a few simple "lessons learned" that hopefully will be useful to you on your organization's AI/ML journey. In my experience, your models will never be perfect.
Now we are moving into the world of'edge computing', in which data is processed close to its source, cutting out the need for it to be sent to the cloud. But computing isn't the only thing taking place on'the edge' – now, AI is being brought to the source of the data as well, allowing'Edge AI' to bring about new standards of speed and intelligence. So, what is Edge AI, what kinds of benefits will it offer, and how will it empower solutions going forward? Currently, the heavy computing capacity required to run deep learning models necessitates that the majority of AI processes be carried out in the cloud. However, running AI in the cloud has its disadvantages, including the fact that it requires an internet connection, and that performance can be impacted by bandwidth and latency limitations.
Some of the hardest changes, are the most important. The massive shift to digitalize industries, sometimes referred to as Industry 4.0 is a great example of difficult, yet critical changes that need to happen. Just as the Internet of Things (IoT) is set to shake up tons of other industries thanks to the increasing outgrowth of cloud and 5G, it's also set to change the stake in the field of manufacturing. The good news: there is so much to be gained from moving to a new form of managing the industrial work process, from saved time to money to new ways of doing business altogether. It takes strategy and the ability to think differently about how manufacturing works--and will continue to work--in the years ahead.
As the 21st century rages on, success and failure of nations depends not only on their citizenry and governmental leadership, but heavily on the technological visions that countries embrace. If a nation takes the approach of sitting back or standing still as automation and Artificial Intelligence advance at ever increasing rates, that nation is destined to be left behind. However, if a country embraces AI and dedicates significant resources and top minds to ethical implementation, that country is destined to be a leader for decades to come. Recently Steve Mills, Chief AI Ethics Officer & Leader for Artificial Intelligence in the Public Sector, and Partner at Boston Consulting Group said quite eloquently "AI has become table stages for global national economic and technological competitiveness. This goes beyond nations capturing a piece of the large and rapidly growing AI market. AI is poised to transform nearly every industry. There is an imperative for nations to position themselves to integrate AI into these sectors. Particularly those sectors that are economically important to them. Failing to do so could erode their competitive position, creating opportunities for other, more technologically advanced nations to fill the void. This is not just a matter of missed upside potential from the new AI market. It's also about downside risk for every other sector that is economically important to a nation."
It is becoming increasingly common for organizations to collect very large amounts of data over time, and to need to detect unusual or anomalous time series. For example, Yahoo has banks of mail servers that are monitored over time. Many measurements on servers/IoT device performances are collected every hour for each of thousands of servers in order to identify servers/devices that are behaving unusually. Python library tsfeature helps to compute a vector of features on each time series, measuring different characteristic-features of the series. The features may include lag correlation, the strength of seasonality, spectral entropy, etc.
When you think of artificial intelligence (AI), what do you envision? For decades, pop culture and science fiction have conspired depictions comprising inspired images of machine-ruled futures and robots accomplishing incredible tasks for human beings. The pictures painted by them are primarily futuristic and incredibly independent. That lays a powerful impression on people. So much so that it can be overwhelming and misleading at times.
European investors have said artificial intelligence (AI) is the most compelling long-term thematic opportunity, according to a survey conducted by CoreData Research. The survey, which was commissioned by WisdomTree and interviewed 440 European investors with approximately €240bn AUM, found 71.4% of respondents highlighted AI as the best thematic opportunity while 59.8% said biotech and 46.6% pointed to cyber security. There are four AI ETFs available on the European market with the largest being the $187m WisdomTree Artificial Intelligence UCITS ETF (WTAI). The $168m Amundi STOXX Global Artificial Intelligence UCITS ETF (GOAI) tracks an index from Stoxx while the $158m L&G Artificial Intelligence UCITS ETF (AIAI) offers exposure to a ROBO Global index and the final strategy offers exposure to big data as well as AI, the $104m Xtrackers Artificial Intelligence & Big Data UCITS ETF (XAIX). AI is in its early stages of adoption and application with investors highlighting the technology to transform industries, services, labour and consumption.
Cobots or collaborative robots are robots that are built for direct contact and interaction with humans like a robot dog or a robotic vacuum. There have been a surprising amount of cobots in space. CIMON was made by IBM, AIRBUS and the DLR (German Aerospace Center). The original CIMON was first proposed in 2016 and went to the ISS in 2018 for 14 months. CIMON 2 went up to the ISS on December 5th, 2019 and it is scheduled to stay there for 3 years.
Data is the new game-changer, everywhere. According to reports, data-driven organizations are 19 times more likely to be profitable. Data and analytics are critical components of digital transformation. Considering the rate at which data is being generated, its analysis is becoming a hefty task. Organizing large volumes of real-time data from several sources is time-consuming and tedious.
In many projects I carried out, companies, despite having fantastic AI business ideas, display a tendency to slowly become frustrated when they realize that they do not have enough data… However, solutions do exist! The purpose of this article is to briefly introduce you to some of them (the ones that are proven effective in my practice) rather than to list all existing solutions. The problem of data scarcity is very important since data are at the core of any AI project. The size of a dataset is often responsible for poor performances in ML projects. Most of the time, data related issues are the main reason why great AI projects cannot be accomplished.