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 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
"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
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
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
4. Bitcoin will prevail, while other
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
"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
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.
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
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
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.