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Artificial Intelligence Can Now Write Amazing Content -- What Does That Mean For Humans?

If you believe anything can and will be automated with artificial intelligence (AI), then you might not be surprised to know how many notable media organizations includingThe New York Times, Associated Press, Reuters,Washington Post, and Yahoo!

It's already used for a variety of content generation needs in our world including business intelligence dashboards, business data reports, personalized email, and in-app messaging communication, client financial portfolio updates and more.

Each content type from social media posts to financial reports to poetry has a unique writing style and structure.

This can have significant service and overall experience benefits for customers.If your company has a workplace savings scheme, thinkofthe 401K portfolio summaries you receive quarterly.

While charts and graphs are visually appealing, it may be a challenge for some people, especially those who aren’t used toanalyzingdata, to extract the important message they should receive from the visualization.

To cut research time and costs, German bankCommerzbankis using artificial intelligence to create equity research reports.This process is not yet completely automated, but the technologyis already able to perform about75 percent of what a human equity analyst wouldhave done.

There are severalautomated journalism applicationsbeing used in newsrooms around the globe from speeding up research to fact-checking, comment monitoring, streamlining workflow, eliminating fake news and, yes, even writing content.

While there are still some glitches to work out before there will be full confidence that machines can actually write with the same creativity and ingenuity as humans, these developments can certainly make you ponder what creativeendeavorswill be uniquely human and what constitutes quality writing.

Is the Future Of Artificial Intelligence Tied To The Future Of Blockchain?

This is mainly because, ultimately, the input of AI is the data through which complex algorithms provide connections, patterns, and useful insight that provide valuable output for individuals and entities across nations: its government, industries, organizations, and academia (NGIOA).

In addition to providing computing power, blockchain technologies also hold the promise of adding structure and accountability to AI algorithms and may help in much-needed areas like security, quality, and integrity of the intelligence AI produces.

Now since big data fuels, artificial intelligence and blockchain generates big data, individually and collectively the future of AI is tied to the future of blockchain.That brings us to an important question: How can blockchain technology infrastructure that we have power AI currently for its computing needs of tomorrow when it is struggling to meet its own computing needs of today?

Now, as technologies like artificial intelligence, machine learning blockchain, and the internet of things begin to require significant computing power, there is a need to not only process computation more efficiently but also to evolve both hardware and software to meet the demand for increased computing power.

Centralized Processing Infrastructure It seems that the current approach to centralized processing infrastructure (e.g., data centers) is not enough to meet the rapidly growing compiling, rendering, and predictive analytics needs of artificial intelligence in a cost-effective and energy-efficient manner.

Neuromorphic Computing Chips Neuromorphic computing may solve this ongoing problem of computing power by doing all the processing and functioning in the chips themselves rather than sending messages back and forth with a more massive server/cloud and by being event-driven and only operating when needed, thereby imitating the brain.

Neuromorphic computing and chips bring the much-needed evolution in computer hardware, allowing us not only to enhance machine intelligence for the complex problems that need to be solved for the future of humanity but also to give us a scalable and sustainable model for the future of blockchain and decentralized systems.

From the current alternative of centralized processing farms to a future filled with the democratization of neuromorphic computing chips, we are witnessing a disruptive effort towards not only reducing the cost of computing through neuromorphic computing chips but also changing the very fundamentals of machine learning and distributed systems.

Using artificial intelligence to predict mortality

An increasing amount of recent research is suggesting that computer algorithms and artificial intelligence (AI) learning can prove highly useful in the medical world.

For instance, a study that appeared a few months ago found that deep learning algorithms can accurately predict the onset of Alzheimer's disease as early as 6 years in advance.

He says, 'Preventative healthcare is a growing priority in the fight against serious diseases, so we have been working for a number of years to improve the accuracy of computerized health risk assessment in the general population.'

'Most applications focus on a single disease area, but predicting death due to several different disease outcomes is highly complex, especially given environmental and individual factors that may affect them.'

Stephen Weng 'This uses computers to build new risk prediction models that take into account a wide range of demographic, biometric, clinical, and lifestyle factors for each individual assessed, even their dietary consumption of fruit, vegetables, and meat per day,' explains Weng.

Furthermore, say the researchers, the results of the new study strengthen previous findings, which showed that certain AI algorithms are better at predicting heart disease risk than the conventional prediction models that cardiologists currently use.

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