TOP Technology Updates for 2018

Deep Learning

Deep Learning, a new area of Machine Learning research, is often clubbed together with it. But with advanced research happening in the areas of Deep Learning specifically, it is important for all AI enthusiasts to understand and keep up with the objective of moving Machine Learning closer to one of its original goals.

Deep learning models are loosely related to information processing and communication patterns in a biological nervous system, such as neural coding that attempts to define a relationship between various stimuli and associated neuronal responses in the brain.

In recent years, some astonishing technological breakthroughs in the field Artificial Intelligence (AI) and its sub-field Deep Learning have begun to train machines to behave like humans.As machines are increasingly emulating complex cognitive functions such as deductive reasoning, inferences, and informed decision-making, robots functioning as humans are a reality in many industry practices today.

However, machines are still behind in articulating the reasons behind their choices or actions. In other words, a machine witness still cannot be used in a court of law to solve a case as it cannot “justify” past actions. The noteworthy achievements in AI applications include the inclusion of neural networks and Deep Learning (DL), which combine unique training opportunities for machines to learn from layers of knowledge, and then to apply that knowledge to achieve particular goals.

Neural networks and Deep Learning penetrate the intensely complex realms of physics, mathematics, statistics, signal processing, Machine Learning, neuroscience, and many others.

Machine Learning ,neural networks, and Deep Learning together represent rapidly advancing core group of technologies within the bigger field of Artificial Intelligence.  Nowadays, machines are capable of instantly solving many complex problems using these techniques, which normally would take capable human brains to figure out over a much longer length of time.

Human society is increasingly relying on smart machines to make decisions and solve day to day problems. This has been made possible due to the presence of neural networks and Deep Learning in AI applications.

There’s still an even longer road ahead for Machine Learning. You can see this in talk about unsupervised learning, improving neural networks, and deep learning.

In fact, as good as today’s machine learning is, it’s still very far from the human ability to learn. Deep learning focuses on rectifying these shortcomings, bringing machines closer to human capabilities in the areas of pattern recognition and analysis, learning, and decision making. While still in its infancy, there’s a lot of interest in deep learning as a more sophisticated subset of Machine learning.

Digital Currencies

Digital currency is a money balance recorded electronically on a stored-value card or other device. Another form of electronic money is network money, allowing the transfer of value on computer networks, particularly the Internet. Electronic money is also a claim on a private bank or other financial institution such as bank deposits.

Digital money can either be centralized, where there is a central point of control over the money supply, or decentralized, where the control over the money supply can come from various sources.

After the bit coin craze rose to a near-fever pitch in the last several weeks of 2017, several investors and analysts in the space see more growing pains for crypto currencies this year.

Here are five predictions for digital currencies, based on those interviews:

1. More institutions will get into crypto currencies.

"Our institutional investor base is very interested in learning more and getting exposure," said Michael Graham, a Can accord Genuity analyst who has published several reports on digital currencies. "One of our major themes is that as we roll out through 2018, it's the year of institutions getting exposure to the space."

The number of institutional-level investment products related to bit coin is increasing.

In addition to the CME and Cboe bit coin futures that launched in December, Cantor Fitzgerald and Nasdaq are planning their own derivatives products. Analysts also expect regulators will approve a bit coin exchange-traded fund in the second half of this year, or in early 2019.

"With the regulated futures markets going live in 2017, the stage is set for ETFs to gain approval in 2018," Nolan Bauerle, director of research at Coin Desk, said in an email. "In fact, the Cboe filed for 6 crypto currency ETFs at the end of 2017 which could go live in 2018. This would dramatically increase how institutional investors can get exposure."

The U.S. Securities and Exchange Commission declined to comment.

2. There will be more regulation and bit coin’s price will drop.

However, in the meantime, regulators will likely try to limit speculation in crypto currencies.

In the last several months, the SEC has become increasingly vocal in warning investors about the risks of crypto currencies. The commission also has suspended trading in some companies due to concerns about their claims regarding their token-related announcements.

"One of the things we'll see [is] enforcement here from the regulators," Can accord’s Graham said. He expects that greater regulation will cause a "major price dislocation event for the whole sector."

Bit coin has soared more than 1,500 percent to near $16,200 over the last 12 months. But it is still down about 18 percent from its all-time high above $19,800 hit in mid-December. Meanwhile, smaller cryptocurrencies have surged hundreds of percent in the last several weeks, bringing the total market value of all digital coins to above $770 billion, according to CoinMarketCap.

Action by regulators could halt those gains. Bitcoin fell more than $2,000 in September when China cracked down on digital currencies.

Spencer Bogart, managing director and head of research at venture capital firm Blockchain Capital, expects that many cryptofunds will not be prepared to handle a monthly decline of 25 percent.

"I think we could easily purge 60-75% of crypto hedge funds in this type of market," Bogart said in an email. "In this environment, funds that can call capital and deploy it counter-cyclically stand to benefit significantly."

More than 120 such funds opened in 2017 for a total of 175 funds, according to financial research firm Autonomous Next.

In contrast to Bogart, Autonomous' global director of fintech strategy, Lex Sokolin, predicts the total number of cryptofunds will nearly triple to 500 this year. But he said the focus will be less on the number of funds and more on assets under management, which he expects to reach $20 billion.

3. It will be a wild, volatile ride.

The contrasting views on the future of crypto funds come as some analysts expect bitcoin to ride an even wilder wave this year.

Ari Paul, chief investment officer of cryptocurrency investment firm BlockTower Capital, predicts that bitcoin will trade at both $4,000 and $30,000 at some point in 2018.

One reason some analysts say bitcoin will ultimately rise further is that investors will bet on a payout from more splits in the digital currency. When some bitcoin developers decide to implement their own upgrade of the bitcoin network, bitcoin investors at the time of the split receive equal amounts of the split-off coin.

Aug. 1's split of bitcoin into bitcoin and bitcoin cash was "a change in the trend," said Ramon Quesada, a vocal member of Spain's cryptocurrency community. Developers "are using the brand bitcoin and they are splitting the main chain. They are making a fork. You create a new chain and you give a new name to this chain."

Bitcoin trades near $16,200, while bitcoin cash trades around $2,600.

"We think we're going to have more forks in 2018 than 2017," Canaccord's Graham said. "Ultimately we think those forks are going to be a short-term tail wind to bitcoin's value and a long-term headwind"

Bitcoin still faces many challenges, such as improving transaction fees and speed.

4. Bitcoin will prevail, while other cryptocurrencies grow.

While bitcoin's price has stagnated in the last two weeks, smaller digital currencies such as ripple, stellar and tron have surged into the ranks of the largest cryptocurrencies by market capitalization.

Erik Voorhees, CEO of digital asset exchange ShapeShift, said that in contrast to bitcoin's dominance on the platform a year ago, about half of transactions on the platform now don't involve the popular digital currency at all.

However, bitcoin should still benefit from the increased interest in the "alt-coins." Analysts also point out that since bitcoin is the most established digital currency, it is often the way new investors access the cryptocurrency space.

"Bitcoin has such magnificent network effects that I don't see another alt-coin that's a little better at payments" or some other function right now, Autonomous' Sokolin said. "One of the top 10 will collapse."

5. Stock investors may get a chance to invest in a digital currency-related IPO.

As interest in digital currencies has grown, the companies involved with the business have become billion-dollar entities. Leading U.S. cryptocurrency marketplace Coinbase, valued at $1.6 billion, has indicated it could pursue an initial public offering.


The use of bit coin and the revitalization of peer to peer computing have been essential for the adoption of block chain technology in a broader sense. The IEEE-CS predictive increased expansion of companies delivering block chain products and even IT heavy weights entering the market and consolidating the products.

 Worried about someone hacking the next election? Bothered by the way Facebook and Equifax coughed up your personal information? 

The technology industry has an answer called the block chain — even for the problems the industry helped to create.

The first block chain was created in 2009 as a new kind of database for the virtual currency bitcoin, where all transactions could be stored without any banks or governments involved.

Now, countless entrepreneurs, companies and governments are looking to use similar databases — often independent of bit coin — to solve some of the most intractable issues facing society.

Block chain allows information to be stored and exchanged by a network of computers without any central authority. In theory, this egalitarian arrangement also makes it harder for data to be altered or hacked.

Block chains assemble data into blocks that are chained together using complicated math. Since each block is built off the last one and includes information like timestamps, any attempt to go back and alter existing data would be highly complicated. In the original bit coin block chain, the data in the blocks is information about bit coin wallets and transactions. The blocks of data in the bit coin block chain — and most of its imitators — are kept by a peer-to-peer computer network.

Industrial IoT

Increasingly, connected sensors being applied to heavy machinery, farmland, and and factories to derive useful insights, make decisions, and optimize systems. And at the same time, there’s been a marked rise in inspection drones and industrial robotics replacing dirty, dangerous, and dull jobs.

Internet of Things (IoT) startups are proliferating to tackle certain industrial use cases, but many companies laying down technical infrastructure and cloud services, and applying AI to spin data into useful insights.

Industrial Internet of Things involves the use of IoT Technologies in manufacturing processes and across supply chains. Alongside data from devices and sensors, Industrial IoT strategies should incorporate machine learning and big data technology, harnessing that combination of existing sensor data, machine to machine (M2M) communication and automation technologies to provide more insight back to the business.

Manufacturing enterprises tend to have large volumes of industrial equipment, all of which needs maintaining. 


 Keeping up with the latest developments in robotics can be difficult. Even harder is knowing where the best sites are for informed coverage on robotics.

As the term “machine learning” has heated up, interest in “robotics” (as expressed in Google Trends) has not altered much over the last three years. So how much of a place is there for machine learning in robotics?

While only a portion of recent developments in robotics can be credited to developments and uses of machine learning, I’ve aimed to collect some of the more prominent applications together in this article, along with links and references.

Some researchers might even argue against a set definition for robot, or debate whether a definition can be relative or dependent upon the context of a situation, such as the concept of “privacy”; this might be a better approach as more and more rules and regulations are created around their use in varying contexts. There’s also some debate as to whether the term robot includes innovations such as autonomous vehicles, drones, and other similar machines.

Most robots are not, and will likely not, be humanoids 10 years from now; as robots are designed for a range of behaviors in a plethora of environments, their bodies and physical abilities will reflect a best fit for those characteristics. An exception will likely be robots that provide medical or other care or companionship for humans, and perhaps service robots that are meant to establish a more personal and ‘humanized’ relationship.

Like many innovative technological fields today, robotics has and is being influenced and in some directions steered by machine learning technologies.



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