Artificial Intelligence, an enigmatic term for technologies that make gadgets and software "smart," is expected to become a bigger part of our lives thanks to advances in computing power, data storage and high-speed networks such as 5G. San Diego is in a strong position to benefit from the expansion of artificial intelligence, according to a study "Measuring the Future: AI and San Diego's Economy" released last week from the San Diego Regional Economic Development Corp. While the study did not pinpoint a specific number of artificial intelligence jobs in the region, it did highlight industries with above-average employment in AI fields. They include telecommunications, information technology, software and transportation. Large companies with operations in San Diego such as Booz Allen Hamilton, Northrop Grumman and ResMed -- as well as smaller businesses such as Lytx, Lockton and Semantic AI -- are among the firms developing artificial intelligence technology in the region.

Artificial intelligence, an enigmatic term for technologies that make gadgets and software "smart," is expected to become a bigger part of our lives thanks to advances in computing power, data storage and high-speed networks such as 5G. San Diego is in a strong position to benefit from the expansion of artificial intelligence, according to a study "Measuring the Future: AI and San Diego's Economy" released last week from the San Diego Regional Economic Development Corp. While the study did not pinpoint a specific number of artificial intelligence jobs in the region, it did highlight industries with above-average employment in AI fields. They include telecommunications, information technology, software and transportation. Large companies with operations in San Diego such as Booz Allen Hamilton, Northrop Grumman and ResMed -- as well as smaller businesses such as Lytx, Lockton and Semantic AI -- are among the firms developing artificial intelligence technology in the region.

Artificial intelligence (AI) is evolving rapidly. According to multinational professional services company, Accenture, businesses spent $306 billion USD on AI applications over the past three years. Despite the advancement of AI, there are currently no specific ethical regulations around the technology--though some governments, including the European Union, are working to establish them. Meanwhile, many organizations are beginning to develop AI standards that will ensure their applications are trustworthy and safe for customers. For example, IBM has taken major steps to build trustworthiness for its AI applications, including the creation of an AI ethics board and AI policies, such as the company's Principles for Trust and Transparency.

More and more enterprises are turning to a promising technology called RPA (robotic process automation) to become more productive and efficient. Successful implementation also helps to cut costs and reduce error rates. RPA can automate mundane and predictable tasks and processes leaving employees to focus more on high-value work. Other companies, see RPA as the next step before fully adopting intelligent automation technology such as machine learning and artificial intelligence. RPA is one of the fastest-growing sectors in the field of enterprise technology. In 2018 RPA software soared in value to $864 million, a growth of over 63%. In the course of this article, we clearly explain exactly what RPA really is and how it works. To help our understanding we will also explore the potential benefits and disadvantages of this technology. Finally, we will highlight some of the most powerful and exciting ways in which it is already transforming enterprises in a range of industries. Robotic Process Automation, or RPA for short, is a way of automating structured, repetitive, or rules-based tasks and processes. It has a number of different applications. Its tools can capture data, retrieve information, communicate with other digital systems and process transactions. Implementation can help to prevent human error, particularly when charged with completing long, repetitive tasks. It can also reduce labor costs. A report by Deloitte revealed that one large, commercial bank implemented RPA into 85 software bots. These were used to tackle 13 processes interacting with 1.5 million requests in a year.

After AI system owners and developers have used a self-assessment to identify inherent AI risks and make informed design decisions, and after they consulted technical playbooks to understand at a granular level what actions to take, they will require software tools to improve upon their AI systems. These tools can be open source or acquired solutions and are designed to address risk areas like fairness, explainability, and robustness. As organizations weigh the costs and benefits of building vs. buying solutions they will require a comprehensive understanding of the software landscape including real costs, customization, and the levels or proficiency required to effectively leverage the tools.

The AI World Summit will be a unique global opportunity to explore how AI's transformational potential can be best used to create a better future. Find out what this means for policy and decision makers, manufacturers, educators and businesses interested in leveraging its potential, for the good of society and the business community. Discover how lives and businesses have to change post Covid; and dealing with tech disruptions associated with issues involving board diversity, SDG/ESG, professional services, manufacturing, future of work, board governance and more! The AI World Summit 2020/21 will be organised on a virtual platform due to Covid-19 pandemic. The Summit will be hosted from Monday 30th November to Friday 4th December 2020 Singapore Time.

This article was published as a part of the Data Science Blogathon. Often we have heard the term Automated Machine Learning or AutoML being used when it comes to machine learning, but what is AutoML, and is the hype around it real? The article is a deep-dive on AutoML, how it can make the power of machine learning available to everyone, and how certain AutoML tools can be tailored to your business needs. Today, companies are incorporating AI into their internal processes to improve the quality of their product pipeline, sales forecasting, predicting customer needs, designing new products/ inventory, and even optimizing their workplaces. As AI/ ML continues to become important for businesses, its challenges have also become more recognizable, particularly when it comes to accessibility of AI and data science.

A recent survey offers insight into the characteristics that differentiate AI overperformers--companies that are realizing the most return on investment from their AI initiatives. Elite athletes know that in order to perform on the field, they must lay the groundwork--through proper nutrition, good sleep, and strength training. As more companies adopt AI technologies, they are quickly learning the same lesson: To get a big return from AI, they should first put in the work. How can CIOs and their C-suite counterparts prepare for effective AI adoption? A recent benchmarking study from the research firm ESI ThoughtLab, cosponsored by Deloitte LLP, offers some answers.

AI can increase efficiency by automating manual, FTE-heavy finance processes such as the order-to-cash cycle. It can also help optimize data profiling, remediation, and integration. Cleaner data, greater accuracy, and more opportunities for efficiencies, which can lead to improvements in days sales outstanding, working capital, and margins. AI can uncover discrete patterns in complex structured and unstructured data, giving finance managers new insights that can support more meaningful analyses while increasing forecasting and financial planning speed and accuracy. By automating customer-facing finance processes such as invoicing, AI can enhance the customer experience, improving customer relationships and brand perception. AI mimics human intelligence to automate activities that nonintelligent technologies cannot, which can free more finance professionals to focus on value-driving activities.

When Deloitte's recent State of AI in the Enterprise study asked AI adopters about their organization's top adoption challenges, "managing AI-related risks" topped the list--tied with integration and data challenges, and on par with implementation concerns.1 And while worry is high, action to ameliorate risks is lagging: Fewer than one-third practice more than three AI risk management activities.2 And fewer than four in 10 adopters report that their organization is "fully prepared" for the range of AI risks that concern them. To investigate whether actively managing AI risks has any tangible benefit, we compared two groups of AI adopters that approach those risks differently: Risk Management Leaders (11%) undertake more than three AI risk management practices and align their AI risk management with their organization's broader risk management efforts, while Risk Management Dabblers (51%) undertake up to three AI risk management practices but are not aligning them with broader risk management efforts.3 The Leaders believe AI has greater strategic importance to their business: 40% see AI as "critically important" to their business today, versus only 18% of the Dabblers--and within two years, those numbers are expected to rise to 63% and 36%, respectively.