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The question mark over AI and intellectual property - Tech Wire Asia

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Artificial intelligence has become a general-purpose technology. Not confined to futuristic applications such as self-driving vehicles, it powers the apps we use daily, from navigation with Google Maps to check deposits from our mobile banking app. It even manages the spam filters in our inbox. These are all-powerful, albeit functional roles. What's perhaps more exciting is AI's growing potential in sourcing and producing new creations and ideas, from writing news articles to discovering new drugs -- in some cases, far quicker than teams of human scientists.


Comprehensive Report on Artificial Intelligence in Energy Market 2020

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Artificial Intelligence in Energy Market research report is the new statistical data source added by A2Z Market Research. "Artificial Intelligence in Energy Market is growing at a High CAGR during the forecast period 2020-2026. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market". Artificial Intelligence in Energy Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors.


Mobility and Event-Based Pricing

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Pricing services is often arcane, taking into account many factors beyond simple supply and demand. As our world becomes more complicated and connected, it's likely that mobility and event-based Pricing will increase and become more complicated. It costs more to ride a subway train during rush hour than it does at 9pm. "Congestion pricing" has traditionally been used to try and steer people who are not in a hurry out of peak hours. The same principle is also applied to tolls, congestion taxes, and opening or closing HOV lanes in specific directions. So, what will the future likely bring?


Using Artificial Intelligence To Tame The Most Volatile Asset Class In History

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We are currently living through one of the most turbulent, financial, technological, and social periods of our lives. This year has brought forward mass global economic uncertainty, unlike anything we have seen before. As a result, monetary policies and their custodians are attempting to adjust without clarity on how decisions will impact both the near and long term future. Financial markets and monetary policies across the globe are unstable and their futures are viewed with broad skepticism. Many of us look to market or economic "experts" for direction, however, we appear to be lacking modern economic precedence to put our current circumstances into the proper context.


Applying AI Towards A Better World: GDP, Jobs Growth & Less Pollution

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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.


Exotec raises $90 million for its warehouse robots – TechCrunch

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French startup Exotec has raised a $90 million Series C round led by 83North, with existing investors Iris Capital and Breega also participating. Other existing investors include 360 Capital. The company has been working on semi-automated warehouses for e-commerce clients. The system is based on tiny robots called Skypods. They roam the floor and go up and down racks to pick up standardized bins of products.


Artificial Intelligence in Insurance

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The insurance industry is seeing a welcome disruption via artificial intelligence (AI), but only a few companies might benefit from this breakthrough. Most organizations lack cognitive technologies to process insight, and this makes the data almost useless. But insurtech companies can connect the potential of the AI data streams available. In this complete introduction to artificial intelligence, you'll be learning: And although artificial intelligence is massively popular, other complex tech topics like big data and deep learning can often cause confusion. So if you want to leverage AI and get the best out of this breakthrough, this article is for you.


Using AI to grow your business and create enterprise value

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Investing in AI can help a business grow, while generating enterprise value. In this article, five experts provide their advice on how businesses can use AI to improve their business and products. "Businesses that deploy AI can expect sales growth through more precisely targeted and relevant customer engagements, more rapid scalability across business operations and greater productivity," says John Michaelis, an expert in the practical aspects of using AI and an experienced business consultant. He is also an active angel investor and board advisor for early-stage AI companies. He provides three essential tips for using AI to grow your business and generate enterprise value.


The Journey of AI & Machine Learning

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Imtiaz Adam, Twitter @Deeplearn007 Updated a few sections in Sep 2020 Artificial Intelligence (AI) is increasingly affecting the world around us. It is increasingly making an impact in retail, financial services, along with other sectors of the economy.


Graph Database: How Graph Is Being Utilised For Data Analytics

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In computing, a graph database (GDB) is a database which utilises graph structures for semantic queries with nodes, edges, and properties to represent and store data. The graph related data items in the store to a collection of nodes and edges, where edges are representing the relationships across the nodes. Graph databases are a kind of NoSQL database, built to address the limitations of relational databases. While the graph model clearly lays out the dependencies between nodes of data, the relational model and other NoSQL database models link the data by implicit connections. Graph databases are the fastest-growing category in all of data management.