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) …
As the name suggests, outliers are datapoint which differs significantly from the rest of your observations. In other words, they are far away from the average path of your data. In statistics and Machine Learning, detecting outliers is a pivotal step, since they might affect the performance of your model. Namely, imagine you want to predict the return of your company based on the amount of sold units. Nice, but what if, among your data, there was an outlier?
Last week, Eko Devices announced a new service that matches ECG and heart sound recordings with clinical data to help pinpoint novel drug-data combinations. The Silicon Valley startup is pitching the platform, called Eko Home, as a resource for clinical trials targeting new therapies. The new platform is already seeing some action. According to the company, an ongoing Mayo Clinic study exploring how carvedilol-based cardiovascular therapies could reduce heart failure or other heart function declines among breast cancer patients undergoing chemotherapy is using the Eko Home platform to drive insights. Eko -- which is best known for its Eko Duo device, a smart remote monitor that's part stethoscope, part ECG -- also said in its announcement that it "expects to offer the drug-data combinations with other life science partners by the end of the year with additional plans to offer its SDK to hospitals and healthcare providers that wish to build the platform directly into their applications."
Researchers have come up with a mobile-sensing system that can track and rate the performance of workers. This research team used a mobile-sensing system to track 750 U.S. workers for one year and through this discovery, the system was able to tell the difference between high performers and low performers with 80% accuracy. You're probably wondering, how this is tracked? The mobile-sensing system has a few distinct pieces. A smartphone tracks physical activity, location, phone use and ambient light.
Natural Language Processing is fast becoming Artificial Intelligence's new frontier which we all are using on daily basis – Siri, Google search, chatbots, automatic translation are just some examples. NLP can offer much more within your organization. We can combine together with your existing business applications tailor-made solution to analyze text, understand the conclusions without human effort, turn unstructured data into tabular data and much more.
Big-data company Databricks Inc. is hoping to empower so-called citizen data scientists to create their own machine learning models with new "Automated Machine Learning" capabilities in its Unified Analytics platform. The AutoML capabilities announced today rely on machine learning too, and are designed to help untrained workers muddle their way through the key steps involved in creating and training machine learning models. Machine learning models are mathematical representations of real-world processes that are used to make predictions, and are created by providing training data for an algorithm to learn from. Creating machine learning models is no easy task, however. It's normally done by highly trained data scientists and requires extensive preparation of the training data that's going to be used.
Increased deployment of Artificial Intelligence around the world has torn open a very public and heated debate. While AI is being used to do things like sentence criminals, determine who should be hired and fired, and assess what loan rate you should be offered, it's also being leveraged to protect against poaching, detect illnesses sooner and more accurately, and shed new insights into fighting climate change. As we continue to develop AmandaAI here at TTT, we increasingly involve ourselves in the field. And as the technology continues to advance, we will continue to take on more and more clients who want to incorporate AI into their software. Since we're helping to create an AI-enabled future, we have a responsibility to explore what exactly that means.
At almost every point in our day, we interact with digital technologies which collect our data. From the moment our smart phones wake us up, to our watch tracking our morning run, every time we use public transport, every coffee we purchase with a bank card, every song skipped or liked, until we return to bed and let our sleep apps monitor our dreaming habits – all of these technologies are collecting data. This data is used by tech companies to develop their products and provide more services. While film and music recommendations might be useful, the same systems are also being used to decide where to build infrastructure, for facial recognition systems used by the police, or even whether you should get a job interview, or who should die in a crash with an autonomous vehicle. Despite huge databases of personal information, tech companies rarely have enough to make properly informed decisions, and this leads to products and technologies that can enhance social biases and inequality, rather than address them.
The innovative power of the German capital in the area of AI is not only noticeable in the high-profile areas of business intelligence and process management, but is also demonstrated by the excellent work of the AI companies which deal intensively with intelligent health and represent about 10 per cent of the Berlin AI ecosystem. AI systems from Berlin are used in a variety of ways: they help in the diagnosis and data analysis of specific disease patterns, but are also used in operation planning and in supporting the internal processes of hospitals. Apps for intelligent data recording and analysis in the field of prevention are being developed in the context of fitness and health. Chatbots, i.e. systems with which people can communicate in natural language, also accompany patients during the healing process. A number of start-ups in Berlin are pushing the boundaries of traditional healthcare with innovative solutions which could also break new ground on the international stage - always at the interface between business and research.
For some time, we've been hearing about how automation -- and lately, artificial intelligence -- is killing jobs, robotizing tasks, and working overtime to make everyone's workdays a total misery. Now, a new study suggests that automation may actually make worklives a little better, taking away the boring and tedious tasks and leaving more interesting stuff for humans. That's the takeaway from a survey of 34,000 workers in 18 countries worldwide, sponsored by Verint, which finds technology may help more than it hurts when it comes to workplace stress. Many of us may have intuitively known this, of course -- the handiness of mobile, the ability to share working docs online, the ability to communicate at a moment's notice, the ability to grab and verify information -- have helped ease many working situations. It's good to see this verified in a large-scale study.
We're now in the back half of 2018, the year artificial intelligence really began to hit the mainstream. As some recent research shows, firms across all business sectors are taking notice and looking to position themselves to reap the benefits of AI. Just over half of enterprises anticipate that machine learning will influence the majority of business operations within the next two years, according to research released in July from HFS Research in partnership with Infinia ML, and 86 percent believe their industry is already being affected by ML. Knowing that, it makes sense that investment in intelligent automation is likely to go nowhere but up. A new KPMG study said that enterprise spending will go from $12.4 billion today to $148.8 billion by 2024, a tenfold increase in less than a decade.