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 new solution is geared towards cutting through red tape and paperwork for borrowers and lenders. Google has announced the launch of Lending DocAI, a dedicated artificial intelligence (AI) service for the mortgage industry. On Monday, Google Product Manager Sudheera Vanguri said the new solution, now in preview, has been designed to transform unstructured datasets into accurate models able to speed up loan applications by accurately assessing a borrower's income and assets. To streamline the loan application process, dubbed "notoriously slow and complex" by Vanguri, Lending DocAI has been built with AI models that specialize in document types related to loans and is able to automate "routine" document reviews so mortgage providers don't have to. The executive says that in turn, this will speed up the mortgage and loan application workflows, including the processing of loan sources and mortgage services.
Rapid advances in technology, connectivity and telecommunications are conspiring to make Africa's large, rapidly growing population a valuable asset for the automation revolution. It is imperative that Africa quickly develop agency in data and artificial intelligence and it will be lucrative for investors who support them by financing Africa's telecom and data backbone. Africa must urgently develop cogent digital strategy. This at first seems fanciful, or even superfluous, given the continent's relative lack of more basic development. Indeed, there are myriad other challenges to which most would assign primacy.
A look at how Zulily is using the latest tools in artificial intelligence, machine learning, and cloud computing to innovate and serve its customers with purpose. Each day at Zulily we add 9,000 products to our online store and process more than 5 billion clicks from online shoppers. That is more virtual inventory than you'll find in the warehouses of many retailers, and it's by design. We've built a supply chain where we hold only some goods: most of the time, we don't purchase inventory until our customers have, so we are able to pass down savings from our unique supply chain down to our customers around the world. To the customer, that means a constantly changing and new shopping experience.
However, what if we add to this and take a more holistic approach to health, describing it as more than just the "absence of illness?" Wellness is a somewhat elusive concept, defined by the WHO as "a state of complete physical, mental, and social well-being, and not merely the absence of disease or infirmity." Can machine learning contribute to our wellness? Health does feature prominently in the overall wellness of a person for the simple reason that being free of illness is the main prerequisite. In other words, suffering from a disease of some kind will trump everything else you do for your wellness. There is no doubt that machine learning has established its role in healthcare with its capabilities.
Collaboration between data scientists, IT, business analysts & developers drive organisation's success The focus on business outcomes has taken on a technological twist. Organisations relying on emerging trends in technology have a sole motive, 'To drive the company towards growth.' As the embrace of innovation continues, it takes a step further for advanced systems to be employed in routine work. Earlier, it was okay for data scientists to get dragged into vague tasks or time-consuming experimentation with a variety of open-source tools in the name of innovation. The collaboration was often an afterthought or extremely difficult to achieve across the enterprise.
Data is the fuel for machine learning, but the data needs to be accurately labeled for the machines to learn. To that end, data training startup Dataloop yesterday unveiled that it's received $11 million in Series A funding to build SaaS data pipelines that combine human supervision of the data annotation process, along with data management capabilities. Today's computer vision models are extremely powerful, and the ones based on deep learning approaches can exceed human capabilities. From self-driving cars navigating in the world to programs that can accurate diagnose diseases in MRI images, the potential uses for Ais built upon convolutional neural networks are astonishingly wide. However, there's a catch (there always is).
Machine Learning has evolved much from the context of an Artificial Intelligence subset to the status of disruptive technology. However, there is a gap between popular belief in machine learning and what machine learning tools can actually accomplish. Though it is perched top on the Hype Cycle, it is harder for people to see where the practical applications of machine learning lie. So when trying to push to find innovative ways to gain even the smallest of competitive advantages, it is better to familiarize oneself with facts about this technology. In essence, machine learning provides systems the ability to self-learn and improve from experience without being explicitly programmed.
AIOps: Is it worth the hype or a necessary cog in IT? AIOps may be a new buzzword, but it is an advanced version of IT Ops that deals with big data operations along with Artificial Intelligence, machine learning, and data analytics. In simple words, AIOps refers to the automation of IT operations artificial intelligence (AI), freeing enterprise IT operations by inputs of operational data to achieve the ultimate data automation goals. It aims to help IT run more efficient operations, make better decisions, and support business productivity. Also, it plays a pivotal role in determining the relationship between the thousands of alerts that all the elements of an IT environment can now generate. This is because most AIOps models offer IT teams with more context, and actionable intelligence. Due to the current pandemic infected market, the hunger for AIOps in the IT landscape has risen.
Remember Facebook's automated personal assistant, M, that was released in a bid to compete with Alexa and Siri? After a series of embarrassing mishaps due to poorly trained algorithms, Facebook abruptly pulled the plug. They weren't alone; chatbots are infamous for putting their metaphorical feet in their mouths. While these debacles are tough to watch, the underlying problem is not artificial intelligence (AI) itself. AI succeeds when underpinned with sound strategy and well-trained models.
It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.