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) …
The economic recession that follows as a consequence of the Covid-19 crisis and in particular the demise of certain sectors of the economy (physical retail, hospitality sector, etc) means that there will be greater pressure on politicians around the world to consider how to stimulate GPD growth in the post-pandemic world. However, there are also increasing pressures on politicians to combat the threat posed by Climate Change. Are the desired objectives of GDP and employment growth as well as reducing pollution at odds with each other? What if there is a pathway to GDP growth with the creation of new jobs and yet at the same time we are able to reduce emissions of Green House Gasses (GHGs)? A report entitled "How AI can enable a sustainable future" by PWC and commissioned by Microsoft (lead authors Celine Herweijer of PWC and Lucas Joppa of Microsoft) estimates that using AI for environmental applications across four sectors – agriculture, water, energy and transport. The report estimated that such applications could contribute up to $5.2 trillion USD to the global economy in 2030, a 4.4% increase relative to business as usual.
It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".
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The Artificial Intelligence (AI) In Fintech Market report predicts promising growth and development during the period 2020-2027. The Artificial Intelligence (AI) In Fintech Market survey report represents vital statistical data represented in an organized format such as graphs, charts, tables, and figures to provide a detailed understanding of the Artificial Intelligence (AI) In Fintech Market in a simple manner. The report covers an in-depth analysis of the Artificial Intelligence (AI) In Fintech market and offers key insights on current and emerging trends, market drivers, and market insights offered by industry experts. The report examines the impact of COVID-19 on market growth. The study provides comprehensive coverage of the impact of the COVID-19 pandemic on the Artificial Intelligence (AI) In Fintech market and its key segments.
While some forecasts will probably get at least something right, others will likely be useful only as demonstrations of how hard it is to predict, and many don't make much sense. What we would like to achieve is for you to be able to look at these and other forecasts, and be able to critically evaluate them. The political scientist Philip E. Tetlock, author of Superforecasting: The Art and Science of Prediction, classifies people into two categories: those who have one big idea ("hedgehogs"), and those who have many small ideas ("foxes"). Tetlock has carried out an experiment between 1984 and 2003 to study factors that could help us identify which predictions are likely to be accurate and which are not. One of the significant findings was that foxes tend to be clearly better at prediction than hedgehogs, especially when it comes to long-term forecasting.
Worldwide Global Artificial Intelligence in Diabetes Management Market report of 2019 provides a detailed market overview as well as industry analysis for / of companies, manufacturers and distributors covering data on gross margin, cost structure, value, sale price and more. It also ensembles the challenges prevalent in this industry vertical and identifies opportunities that will further aid business expansion. Further, the report revisits all areas of the business to cover the impact of COVID-19 pandemic so as to assist stakeholders in devising new strategies and reinforcing their position in the market.
By utilising machine learning and numerical text processing techniques, Swiss Re has been able to generate a "predictive view" of motor frequency developments in several markets. In a recent conversation with Nikita Kuksin, Hhead of modelling within Casualty R&D, Miriam Hook, vice president Global clients and Surbhi Gupta, assistant vice president, casualty R&D at Swiss Re, it was explained to us how these alternative approaches were able to provide added granularity to existing data. "We intended to develop an alternative to traditional actuarial calculation methods that would give us an "external perspective" on claims frequency within our motor portfolio and allow us to predict motor frequency developments in several motor markets," said Kuksin, who leads the modelling team within the casualty research and development department at the Swiss Re Institute. Gupta, who prior to her current role served at Swiss Re for three years' as a data scientist, explained how these methods were brought into fruition by first checking the status quo of frequency developments against external data, before then explaining motor frequency using external data to generate factors that could be projected into the future. "These are complex objectives, requiring solid data sets and robust analytics," Gupta explained.
We already covered in a previous post, how important it is to deal with uncertainty in financial Deep Learning forecasts. In this post, we'll attempt a first introduction on how we deal with explainability. Neural networks have been applied to various tasks including stock price prediction. Although highly successfully, these models are frequently treated as black boxes. In most cases we know that the performance on the test data is satisfying, but we do not know why the model came up with a specific output.
Big data has become a key driver for enterprises to enhance their sustainability in the business world. With more data being produced and stored than ever before, the need for more efficient, effective, and precise tools has grown too. Predictive Analytics is one such powerful tool. Predictive Analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. It analyzes current and historical facts and situations to make predictions about future or otherwise unknown events.