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) …
The Internet of Things (IoT) has been a buzzword in recent years, given its role in shaping smart cities and consumer lifestyle across Asia Pacific. According to IDC, the region – seen as the frontline for IoT adoption – will account for 8.6 billion of 29.5 billion connected devices globally by 2020. Industries are increasingly realising how IoT can accelerate digital transformation in organisations, enhance data connectivity and exchange across the network, and provide new opportunities for virtual and physical systems to become more integrated. Across the region, companies have been applying contemporary data science techniques to deliver applications, products and services through smartphones. The combination of IoT, Artificial Intelligence (AI), machine learning and predictive analytics is currently creating big economic value for organisations across the region.
Modern agriculture has come a long way over the last two decades. With many technological advancements, the practices in farming have evolved from traditional methods to digital tools. Now, advancements in machine learning and artificial intelligence is being used in this field to ensure growing demands are met by optimising resources. The Indian government think tank NITI Aayog had recently unveiled a discussion paper which addressed the national strategy on AI and other emerging technologies to be focussed on five core sectors. Agriculture was one of the key sectors mentioned in the draft, because the use of new tech would enhance farmers' income, increase farm productivity and reduce wastage.
Cisco is betting heavily that artificial intelligence and machine learning will play an enormous part in future networks and data centers. How far and what roles those technologies play may be the biggest questions but the stakes are clearly in the ground. For its part, Cisco this week rolled out a server system targeted at supporting machine learning and AI applications, but it is really just the tip of the iceberg of the network giant's move toward both technologies. For example, in a recent interview Roland Acra, senior vice-president and general manager of Cisco's data center business, noted a number of ways Cisco is utilizing machine learning in particular to drive networking changes. Central to Cisco's push is being able to gather metadata about traffic as it passes without slowing the traffic, which is accomplished through the use of ASICs in its campus and data-center switches.
Creating agents that can learn like children is one of the ultimate goals of artificial intelligence. Disciplines such as reinforcement learning(RL) are fully devoted to create self-learning models that can use a combination of punishment and reward feedback to master a new task. However, most RL techniques suffer from two main challenges. One very well known is the exploration-explotaition dilemma in which an agent needs to decide how many resources to dedicate to exploring the environment vs. taking specific actions. The other and far less know challenge of RL methods is what I like to call the prior knowledge imbalance dynamic.
At the Cannes film festival earlier this year, a conservation initiative aimed at giving back to the animals captured for film and television was announced by Australian production company Finch, backed by the United Nations Development Programme (UNDP). The Lion's Share sees advertisers contribute 0.5 percent of their media spend to the fund for each advertisement they use featuring an animal. In just four months, the initiative has gained the support of David Attenborough as its ambassador. Data61, the innovation arm of the Commonwealth Scientific and Industrial Research Organisation (CSIRO), has also jumped on board the initiative as Finch's science and technology partner. "[The Lion's Share] is a great example of using machine learning and artificial intelligence not just for productivity gains, but to create new value that wasn't possible before," Data61 chief Adrian Turner told D61 LIVE in Brisbane on Tuesday.
Pattern matching and predicting an exigent need in hospitals is a difficult task for skilled medical staffs, but not for AI and machine learning. Medical staffs do not have the luxury of observing each of their patients on a full-time basis. Although incredibly good at identifying the immediate needs of patients in obvious circumstances, nurses and medical staffs do not possess the capabilities of discerning the future from a complex array of patient symptoms exhibited over a reasonable period. Machine learning has the luxury of not only observing and analyzing patient data 24/7, but also combining information collected from multiple sources, i.e. historical records, daily evaluations by medical staff, and real-time measurements of vitals such as heart rate, oxygen usage and blood pressure. The application of AI in the assessment and prediction of imminent heart attacks, falls, strokes, sepsis and complications is currently underway all over the world.
Agriculture is always modernizing, but most farmers struggle to collect data that's useful--or to analyze it in useful ways. That's changing: emerging tools for data collection and analysis are helping boost yields and make farming more sustainable, according to Sam Eathington, chief science officer at the Climate Corporation. In the next five to 10 years, "we're going to see an explosion of sensors and collection of data from the farm," Eathington said during his talk at MIT Technology Review's EmTech conference today. The Climate Corporation--originally founded in 2006 by a pair of former Google employees and now owned by German chemical giant Bayer--has developed tools to gather information from a variety of sources, including sensors on farming equipment as well as in the field. The data from these disparate sensors is then analyzed in the cloud.
In an industry such as mining where improving efficiency and productivity is crucial to profitability, even small improvements in yields, speed and efficiency can make an extraordinary impact. Mining companies basically produce interchangeable commodities. The mining industry employs a modest amount of individuals--just 670,000 Americans are employed in the quarrying, mining and extraction sector--but it indirectly impacts nearly every other industry since it provides the raw materials for virtually every other aspect of the economy. It's already been 10 years since the British/Australian mining company Rio Tinto began to use fully autonomous haul trucks, but they haven't stopped there. Here are just a few ways Rio Tinto and other mining companies are preparing for the 4th industrial revolutions by creating intelligent mining operations.
Recently, google launched a Dataset search – which is a great resource to find Datasets. In this post, I list some IoT datasets which can be used for Machine Learning or Deep Learning applications. But finding datasets is only part of the story. A static dataset for IoT is not enough i.e. some of the interesting analysis is in streaming mode. To create an end to end streaming implementation from a given dataset, we need knowledge of full stack skills.
This article is about a Python package that has some data processing, visualizations and so on. Symbionic startup created a python package that has data for biometric control algorithms. You are able to install it via "pip" and then just import it into Python. This library can be used for loading raw sensor data. The OYMotion company gave this startup some sample data.