Before the concept of cloud computing came into the picture back then even if a website needs to be hosted companies had to buy huge servers and maintain them. It was a huge cost and inefficient workforce diversion for the companies which wanted to focus on the actual task at hand rather than the maintaining of these servers. Some other companies saw this as an opportunity which went ahead and bought these huge servers and had a huge collection of servers and rented them out to other companies. It is a win-win for everyone since it is cheaper and easier for the companies that wanted to focus on their application/product rather than maintaining these servers. We all use electricity, how do we pay for this we pay according to the number of units used.

When you work in python, you'll be working with several frameworks and many of them work only on specific versions of python. Now imagine downloading a new version of python and then installing it for every framework you want to work with. Meet Anaconda which allows you to run several versions of python. It comes pre-installed with several data sciences and machine learning frameworks. Pip-env is also a way of maintaining several versions of Python and comes pre-installed with Python.

In the coming years, surviving in either industry or academics field with deep learning and machine learning abilities will most likely play an important role. It can seem difficult to grasp the latest developments in artificial intelligence (AI), but if you're keen to learn the fundamentals, you can break many AI technologies down to two concepts: machine learning and deep learning. These terms also seem to be identical buzzwords, hence understanding the distinctions is significant. Deep learning is a concept of artificial intelligence (AI) that mimics the functioning of the human brain in data processing and the development of patterns for decision-making use. It is an artificial intelligence subset of machine learning with networks that learn without being managed from unstructured or unlabeled data.

What is Rebecca Artificial Intelligence? Rebecca Artificial Intelligence software uses AI to increase the productivity of factory machinery. The program works based on learning: besides "observing" the working machinery, users can also add more machine-related data through its interface, which is designed to be intuitive for ease of use. The more information that Rebecca Artificial Intelligence receives, the more efficiently it can intervene to correct possible errors in the machinery.

From smart appliances and chat bots to public safety and intuitive logistics, artificial intelligence impacts our daily lives. AI reaches across platforms at enterprise scale, offering convenience, increasing productivity, and propelling innovation. The IU Online BA in Artificial Intelligence can put you at the forefront of this emerging AI landscape. As a student in this program, you develop the AI skills that employers are seeking in machine learning, bot development, robotic process automation, and cognitive computing. Problem solving lies at the core of the program, as you learn how to apply artificial intelligence, bot platforms, and frameworks to automate robotic and cognitive processes from start to finish.

Artificial Intelligence and Machine Learning applications have over the past few years made the process of learning a fun and interactive experience. Thanks to the advancements in the domain, students as well as educational institutions are now equipped with customized software tools, powered with virtual and augmented reality. At a time when remote learning has become an indispensible part of our education system, the role of technology cannot be ignored when it comes to ascertaining or determining the learning curve of any student. The foremost benefit of AI & ML with regard to the learning curve for students is that now they can generate personalized learning paths for themselves, which would help them concentrate on their shortcomings as well as strengths. On the basis of existing set of data, the technology of AI & ML can predict the outcome, becoming resourceful during the preparation of tests and examinations.

Artificial intelligence holds the key to an era of innovation and is increasingly becoming pervasive in our lives. Businesses across sectors are leveraging the transformative potential of AI for data-driven decision-making. However, at the core of the AI revolution lies the need for large training datasets, something that most enterprises are struggling to address. Data annotation and labelling services play a critical role in bridging this gap by helping enterprises with quality training data for their AI models. This report attempts to highlight the huge potential that India has to become the data annotation and labelling hub for the world. The study demystifies the data annotation landscape, market serviced by India, and the potential impact that the industry can create both in terms of job creation and accelerating India’s AI readiness. Key Highlights Data Annotation Overview: A Global Perspective Global data annotation spend on third-party solutions is estimated to be 7X by 2023 as compared to 2018, constituting about 1/4th of the total spend on annotation The two building blocks needed for data annotation services are a trained workforce and an efficient annotation platform to operationalize labelling Crowdsourced platforms and managed service providers offer different value propositions depending on client requirements across cost, scale, security, quality and agility ~70% of global players are in the intermediate to advanced phase of maturity with sustainable at-scale and diversified offerings The India Story: Annotation Landscape and Trends The data annotation market serviced by India in FY20 valued at ~USD 250 Mn – with ~60% of the revenues derived from US clients The India market revenues are derived from multiple business models with managed services contributing ~65-70% of the overall market Indian MSPs leverage either a dedicated workforce or a BPM partnered model with >80% of the employees from non-metro cities India’s competitive edge stems from its decades of service delivery experience and is driven by key pillars of cost, infrastructure, talent and innovation Indian Players: Current Maturity, Challenges and Opportunities Data annotation industry in India is still gathering pace with ~75% players in the initial and growth phase Challenges restricting Indian MSPs’ access to markets include data privacy, lack of cultural context & growing demand for non-English language data labelling COVID-19 posed several challenges for MSPs with an FTE workforce; requiring the MSPs to alter their operating model to ensure business continuity Opportunities for players are driven by advanced market access, expanded offerings, catering to advanced annotation and sector specific needs Outlook and Recommendations Data annotation market serviced by India can exceed USD 7 Bn. by 2030 with a potential of up to 1 Mn. workforce engaged via full-time and part-time employment models Roadmap for service providers comprises of ensuring existing capabilities and developing new capabilities for them to unlock maximum value Impact of data annotation on India – Job growth and accelerated AI readiness, transitioning India to an AI-ready nation Boosting domestic AI demand, unlocking public sector datasets, developing strong data policies and infrastructure and capital support to MSMEs will fuel the India data annotation market growth

That's a higher percentage growth than 2018 to 2019 when the volume of publications increased by 19.6 percent. China continues to be a growing force in AI R&D, overtaking the US for overall journal citations in artificial intelligence research last year. The country already publishes more AI papers than any other country, but the United States still has more cited papers at AI conferences -- one indicator of the novelty and significance of the underlying research. These figures come from the fourth annual AI Index, a collection of statistics, benchmarks, and milestones meant to gauge global progress in artificial intelligence. The report is collated with the help of Stanford University, and you can read all 222 pages here.

Deciphering what flavor notes are present in a given coffee is more art than science. Tasters spend hours upon hours training their palates, translating the sensory information through the lens of past (highly subjective) flavor experiences to arrive at descriptors that best align with the original sensory input. But one startup is looking to take a more scientific approach to identifying flavors in coffee. The company is called Demetria and they have created an app to "detect a specific and high value sensory ("taste") profile of green coffee." In a press release announcing the company, Demetria touts itself as "the first AI-powered taste and quality intelligence [software as a service] startup for the coffee supply chain."

Every organization deploys certain strategies to stand out in the market and to gain a reasonable market share. However, effective customer support can be a real game-changer. To support this statement, let us look at some of the facts released recently by Salesforce.com. It found that 86% of customer service leaders from over 300 manufacturing companies have said that the customer service they offered was a key differentiator. Further, the report claims that seven out of ten consumers, along with 82% of business buyers, made it clear that it has become easier for them to find options for their purchase if they are dissatisfied with customer service. It has been established so far that customer service is an integral part of a business and that they can't afford to turn a blind eye to it.