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) …
Manufacturing products can be very expensive and a complex process for those businesses that do not have the right tools and resources to develop quality products. In the prevailing time, artificial intelligence and machine learning have become more prevalent in producing and assembling items, helping in reducing cost and time of production. In fact, 40% of all the potential value that can be created by analytics today all come from the AI and ML techniques. In totality, machine learning can account between $3.5 trillion to $5.8 trillion in the annual value -- according to Mckinsey. In the last 5 years, it has been recorded that exponential technologies can help build robust and rapid models that drive functional improvements.
This is an updated version. The Godfathers of AI and 2018 ACM Turing Award winners Geoffrey Hinton, Yann LeCun, and Yoshua Bengio shared a stage in New York on Sunday night at an event organized by the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020). The trio of researchers have made deep neural networks a critical component of computing, and in individual talks and a panel discussion they discussed their views on current challenges facing deep learning and where it should be heading. Introduced in the mid 1980s, deep learning gained traction in the AI community the early 2000s. The year 2012 saw the publication of the CVPR paper Multi-column Deep Neural Networks for Image Classification, which showed how max-pooling CNNs on GPUs could dramatically improve performance on many vision benchmarks; while a similar system introduced months later by Hinton and a University of Toronto team won the large-scale ImageNet competition by a significant margin over shallow machine learning methods.
Robotic process automation has generated a lot of buzz across many different industries. As businesses focus on digital innovation, automation of repetitive tasks to increase efficiency while decreasing human errors is an attractive proposition. Robots will not tire, will not get bored, and will perform tasks accurately to help their human counterparts improve productivity and free them up to focus on higher level tasks. Beyond simple RPA, intelligent automation can be achieved by integrating machine learning and artificial intelligence with robotic process automation to achieve automation of repetitive tasks with an additional layer of human-like perception and prediction. By design, RPA is not meant to replicate human-like intelligence.
TTEC Holdings, Inc., a leading digital customer experience technology and services company focused on the design, implementation and delivery of transformative solutions for many of the world's most iconic and disruptive brands, announced a strategic partnership with Pegasystems, Inc., the software company empowering digital transformation at the world's leading enterprises. This partnership will empower clients with industry-leading digital transformation solutions to optimize customer experiences within their contact centers. With the partnership, Pega's world-class intelligent automation and customer engagement suite, combined with TTEC's Customer Experience as a Service platform, will provide the backbone of optimized, digitally driven employee and customer experiences managed by TTEC Digital. The two market leaders will leverage their decades of experience to deliver best-of-breed human and AI-powered intelligence across the customer lifecycle. Together, TTEC and Pega are uniquely positioned to remove the technical and operational obstacles that stand in the way of great experiences for a brand's customers and employees.
FP Alpha, an AI-powered technology solution for financial advisors, was launched today by Andrew Altfest, President of Altfest Personal Wealth Management. FP Alpha is the first comprehensive wealth management platform to utilize artificial intelligence (AI). The software enables advisors to transform financial planning into comprehensive wealth management by streamlining financial planning processes and offering more services to clients. Designed to easily integrate with financial planning software on the market today, the firm's technology allows advisors to scale efficiently and decrease the burden of time-consuming spreadsheets, checklists and labor-intensive tasks – enabling them to save time in the process and add more value to client relationships. By reducing laborious, manual tasks within wealth management services and financial planning processes, FP Alpha helps advisors deploy high impact and personalized recommendations to clients in a scalable, intelligent and cost-efficient manner.
This post is the first in a series that will highlight the similarities and differences of AI software development with regards to non-AI software development. In this article, we will focus on the software architecture of a complete AI solution. Developing Artificial Intelligence (AI) software components using techniques such as Deep Learning (DL) or Machine Learning (ML) implies some changes in the way you produce a software solution. In "traditional" software development (later written non-AI software), software engineers write source code in a programming language (python, java, C, etc.) to implement an algorithm. On the other hand, AI software development does not involve that much coding.
Artificial intelligence (AI), a broad field that deals with the ongoing pursuit to render machines capable of performing intelligent tasks, has taken the academic and industrial worlds by storm in a breathtakingly short time span. These days, when you engage in the daily surf of your favorite news website, some mention of AI will probably ensue. Machine learning, currently the most prominent subfield of AI, focuses on algorithms that learn from data, with deep learning--employing artificial neural networks with several hidden layers--being the jewel in the crown. From playing Go to processing radiological images, machine learning's success and breadth of scope is undeniable. Yet we mustn't forget that the parent field of AI has birthed many other offspring.
TensorIoT, Inc. will be leading an event demonstrating the career opportunities of the Tech Industry to elementary aged Los Angeles students. TensorIoT will discuss the various career roles within the Tech Industry, what each role consists of and the excitement and creativity that comes along with it. TensorIoT believes it is vital to teach our youth of these career opportunities and encourage them to pursue this path in order to secure the future and have educated, passionate developers. TensorIoT will be bringing a handful of their amazing employees to speak to 3rd-5th graders about what brought them into the Tech Industry, why they love their jobs, and the fun things they get to do daily. Through demonstration the company aims to educate them on what Artificial Intelligence is, IoT, as well as Machine Learning.
Do you think developers write the code of entire software on their own? Well, they can't even if they want to. Software code is complex and contains millions of logics. Developers can end up getting confused and mess up the entire software. Well, that's where application programming interface (APIs) comes to the rescue.
When new industry buzzwords or phrases come up, the challenge for people like us who write about the topic is figuring out what exactly a company means, especially when it uses the phrase to fit its own marketing objective. The latest one is edge artificial intelligence or edge AI. Because of the proliferation of internet of things (IoT) and the ability to add a fair amount of compute power or processing to enable intelligence within those devices, the'edge' can be quite wide, and could mean anything from the'edge of a gateway' to an'endpoint'. So, we decided to find out if there was consensus in the industry on the definition of edge vs. endpoint, who would want to add edge AI, and how much'smartness' you could add to the edge. First of all, what is the difference between edge and endpoint? Well it depends on your viewpoint -- anything not in the cloud could be defined as edge. Probably the clearest definition was from Wolfgang Furtner, Infineon Technologies' senior principal for concept and system engineering.