We have known for some time that the number of signals coming from your IT systems surpassed the ability for humans to keep track of them years ago. Machines can help and have been for some time. The advent of artificial intelligence and machine learning has accelerated that ability and today, Cisco announced that it is using these technologies to help customers find failures before they become major issues. Cisco's solutions are similar to others out there using AI and machine learning to help augment humans' ability to sift through the mass of information being thrown at us by these systems. The company is introducing two new sets of services to address this need.
We have known for some time that the number of signals coming from your IT systems surpassed the ability for humans to keep track of them years ago. Machines can help and have been for some time. The advent of artificial intelligence and machine learning has accelerated that ability and today, Cisco announced that it is using these technologies to help customers find failures before they become major issues. Cisco's solutions are similar to others out there using AI and machine learning to help augment humans and our ability to sift through the mass of information being thrown at us by these systems. The company is introducing two new sets of services to address this need.
The most important phones of the year have already been announced, but one company might still be able to pique our interest. Huawei unveiled its AI-focused Kirin 970 processor at IFA, saying the chip's real world benefits would be shared at the launch of its next flagship. Now, the company is ready to reveal how the Kirin 970 performs in a phone. The Huawei Mate 10 and Mate 10 Pro were designed around AI -- so much so that Huawei wants to call them "intelligent machines." We don't know how much these intelligent machines will cost yet, but Huawei told Engadget to expect the prices to be competitive.
As a result, it took several years for Big Data to evolve from cool new technologies to core enterprise systems actually deployed in production. In our conversations with both buyers and vendors of Big Data technologies over the last year, we're seeing a strong increase in budgets allocated to upgrading core infrastructure and analytics in Fortune 1000 companies, with a key focus on Big Data technologies. What was previously a headwind for Big Data technologies (hard to rip and replace existing infrastructure) is now gradually turning into a tailwind ("we need to replace aging technologies, what's best in class out there?"). The first few months of 2017 saw a flurry of announcements of big funding rounds for growth stage Big Data startups: Looker ($81M Series D), InsideSales ($50M Series F), DataRobot ($54M Series C), Confluent ($50M Series C), Collibra ($50M Series C), Uptake ($40M Series C), WorkFusion ($35M Series D) and MapD ($35M Series B).
Rajamani says there are basically two forms of digital skills: 1) Digital experience-related skills – these skills revolve around designing and implementing digital customer experiences for clients; and 2) digital infrastructure-related skills – these skills can be bucketed into 5 categories linked to modernizing legacy IT infrastructure and IT processes – automation, cloud, devOps, cyber security and data analytics. Udacity, another online portal, found that the maximum demand for online courses in India is from Bangalore closely followed by Delhi, Mumbai, Hyderabad, Pune and Chennai. Hyderabad wants to learn deep learning, data analysis and artificial intelligence. Chennai wants to learn data analysis, deep learning and android, while Pune is witnessing maximum search demand for deep learning, followed by android and artificial intelligence.
Late September, the 4th Intelligent Assistants Conference took place in San Francisco. Over the years, this conference has become a flagship event for the space, combining experience sharing from users and panel discussions with vendors. Natural Language Processing (NLP) and Natural Language Understanding (NLU) are at the "peak of inflated expectations." Assistants are now playing a more strategic role, enabling digital engagement and improving the customer experience.
Plenty of enterprise companies use combinations of automated data science, machine learning, and modern deep learning approaches for tasks like data preparation, predictive analytics, and process automation. HyperScience recently came out of stealth with their $18 million Series A in December of 2016 to completely automate back office operations like form processing and data extraction through AI. Companies need the right format, volume, and understanding of data before they can effectively deploy artificial intelligence solutions, making data science and management critical to any ambitious enterprise. Diffbot uses a combination of AI techniques like computer vision, NLP, and machine learning to enable developers to extract and understand objects from any web page.
As these bots promise quick response times, customer queries are handled efficiently, improving customer satisfaction and experience with a business. As these interactions are completely private and are of interest to both the businesses and consumers, the app helps businesses avoid wasting time due to handling multiple customer support channels, and provide quick service resolution to end consumers. As IoT solutions help businesses lower operational costs, and increase productivity and expansion, businesses are actively adopting such solutions to engage customers with delightful experiences. As the device monitors fitness activities in real time and helps users directly upload the same to social platforms to receive appreciation, it motivates other prospective users to buy the device and engage with the brand, thus, improving business prospects.
The traditional healthcare system has enormous amounts of patient data (medical records, images, videos, and ICU signals) funneling into predictive-analytics systems that learn and detect trends to improve patient care. They let medical-device engineers and researchers prototype and implement advanced algorithms, analyze large amounts of varied types of data quickly and effectively, and develop/deploy new machine-learning models without coding them from scratch. The second framework that helps us understand this landscape is the machine-learning algorithms that add intelligence into this entire system and help transform data into actionable insights. Smart algorithms built using machine-learning techniques enable the extraction of meaningful information from large amounts of text data, signals, images, and videos to automate and accelerate diagnostic capabilities.
The goal of newly-formed AI teams is to build intelligent systems, focused on quite specific tasks, that can be integrated into the scalable data transformations of Data Engineering work and the data products and business decisions of Data Science work. The differences between Artificial Intelligence, Data Science, and Data Engineering can vary considerably among companies and teams. Artificial Intelligence, or AI, focuses on understanding core human abilities such as vision, speech, language, decision making, and other complex tasks, and designing machines and software to emulate these processes. These models typically require very large datasets, so while efficient manipulation and use of large amounts of data is a fundamental aspect of Data Engineering work, it is crucial for state-of-the-art AI systems.