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Choosing the Right AI approach for your business

#artificialintelligence

Companies often kiss more frogs than princes when it comes to artificial intelligence investments. Much has changed this year. The new normal brought about by the COVID-19 pandemic has affected everyone and everything. Globally, companies have been forced into crisis management mode: adapting, retooling and training employees to work remotely while adopting new technologies to stay productive. During all of this, corporate CEOs are trying to accomplish three primary objectives for their business: maximize growth, minimize risk, and protect margins. They have become even more challenging in manufacturing, considering the new work rules, the changing corporate remote work policies, and the use of conventional manufacturing technologies.


"How AI will power the next-gen applications in Connected Industries (CI)"

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Hiroshige Seko, the minister of Economy Trade and Industry (METI) of Japan introduced a new concept for their roadmap to realize'Society 5.0' the future urbanism as the next big thing in industries. He mentioned that we require another industrial revolution using advanced technological innovations including, AI, IoT, and Big Data; this would be'Connected Industries.' This was the inception of'Connected Industries' as introduced by Hiroshige with the impact on future lives. Artificial Intelligence or AI will be on a next-level role in this development, with a more significant impact on each ecosystem entity. Before moving ahead to understand the role of AI in the'Connected Industries', let's first understand AI and its applications.


How Organizations Can Build Analytics Agility

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In an era of constant change, companies' data and analytics capabilities must rapidly adapt to ensure that the business survives, never mind competes. Organizations seek insights from their data to inform strategic priorities in real time, yet much of the historical data and modeling formerly applied to predict future behavior and guide actions are proving to be far less predictive, or even irrelevant, in our current normal with COVID-19. In order to survive through crises, proactively detect trends, and respond to new challenges, companies need to develop greater analytical agility. This agility comes from three areas: improving the quality and connections of the data itself, augmenting analytical "horsepower" at the organization level, and leveraging talent that is capable of bridging business needs with analytics to find opportunity in the data. Get monthly email updates on how artificial intelligence and big data are affecting the development and execution of strategy in organizations. The quest for better data is not new, but the cost of not having it is easier to substantiate and understand in a time of crisis.


IBM Launches Artificial Intelligence Centre In Brazil

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Introduced in 2019, by IBM, Brazil has launched the largest research facility, that focuses on artificial intelligence, through a collaboration between the private and public sector. The Artificial Intelligence Center (C4AI) is supported by investments made by IBM along with the São Paulo Research Foundation (FAPESP) and the University of São Paulo (USP). This AI centre -- C4AI has been established to tackle five significant challenges that are related to health, the environment, the food production chain, the future of work and the development of NLP technologies in Portuguese. Along with this, it will also aid in projects relating to human wellbeing improvement as well as initiatives focused on diversity and inclusion. The total investment in the AI centre will reach $20 million over the next ten years, which will be split among the investors. The USP will contribute $1 million to cover costs related to the physical set-up of the space, as well as over 70 lecturers and staff to run the centre.


New report shows how AI in health is critical for COVID-19 response and recovery

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A major new report led by the Novartis Foundation and Microsoft shows how investment in data and AI is critical to drive the health system improvements needed to respond to and recover from the COVID-19 pandemic and the world's other greatest healthcare challenges. Reimagining Global Health through Artificial Intelligence: The Roadmap to AI Maturity was developed by the Broadband Commission Working Group on Digital and AI in Health, which the Novartis Foundation and Microsoft co-chair. Based on a landscape review of over 300 existing use cases of AI in health, the report shows how AI is already disrupting health and care. It then presents a roadmap to help countries use AI to transform their health systems from being reactive to proactive, predictive, and even preventive. Low- and middle-income countries (LMIC) that grapple with systemic health challenges such as a shortage of health workers, underserved populations, rapid urbanization and disinformation have the most to gain from AI – but they also have the most to lose.


How AI and Machine Learning Can Transform the Chemical Industry

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The chemical industry is -without question- one of the most important industries in the world. Not only do 90% of our everyday products contain chemicals, but the industry also employs approximately 10 million people. Naturally, they were one of the first to embrace digital technologies such as process control systems or sensors which have a long tradition in production. According to Frithjof Netzer, Senior Vice-President and Project Lead 4.0 of BASF: "A lot of energy and momentum in the field of digital can be observed, Chemicals are catching up. It is not the question if, but rather what and how it will be done." A continuous digital transformation plays a crucial role in several key aspects of the industry.


Q&A: How to train your data at exascale speed - SiliconANGLE

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Having data and having insights are two very different things. To transform data into information that can actually help drive better decisions and scientific breakthroughs is a proactive task. So what are the steps data scientists recommend to turn that stagnant data lake into a sparkling flow of insights? "Step back from the data questions; the infrastructure questions; all of these technical questions that can seem very challenging to navigate," said Arti Garg (pictured), head of AI solutions and technologies at Hewlett Packard Enterprise. "And first ask: What problems am I trying to solve? It's really no different than any other type of decision you might make in an organization."


Are commercial insurers doing AI right?

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Artificial Intelligence (AI) has been in almost every technology-based headline over the past 24 months. If an incumbent technology provider or a newly emerging InsurTech organization wants to grab attention – well, just insert AI in the first few lines of the description. Or, better yet, insert AI in the product or organization name. In fact, AI does hold exceptional business promise, and there are numerous proven use cases. But AI is a complicated topic.


Hacking Super Intelligence

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These attacks are not similar to the traditional ones and can't be countered with traditional measures. Today there's only a trickle of these attacks, but in the coming decade, we may be facing a tsunami. To prepare, we need to start securing our AI systems today. I wanted to start with the premise to AI Security, only to realize that I'm risking a cliché on how disruptive AI is. Just to get it off the table, I'll mention that AI is not only part of our daily life (search engine suggestions, photo filters, digital voice assistant).


The Emerging Architectures for Modern Data Infrastructure

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As an industry, we've gotten exceptionally good at building large, complex software systems. We're now starting to see the rise of massive, complex systems built around data – where the primary business value of the system comes from the analysis of data, rather than the software directly. In fact, many of today's fastest growing infrastructure startups build products to manage data. These systems enable data-driven decision making (analytic systems) and drive data-powered products, including with machine learning (operational systems). They range from the pipes that carry data, to storage solutions that house data, to SQL engines that analyze data, to dashboards that make data easy to understand – from data science and machine learning libraries, to automated data pipelines, to data catalogs, and beyond.