Mobile Micro Apps Boosting Employees Productivity

Today’s technology market offers numerous options for businesses to connect with employees wherever they are. With an average person using their mobile phone for more than 2-3 hours per day, organizations have an opportunity to reach employees in one of the most highly trafficked channels. With smart solutions managers who curate mobile technology to communicate will see the highest levels of employee engagement. With a minute to spare people in the field typically turn to their mobile device to fill in even the shortest gaps between tasks.

Mobile application development companies usually tend to build apps that aim to do everything at once. Generally, mobile apps are nothing but an abbreviated form of prevailing websites or desktop apps that are intended to perform all the same tasks as their larger counterparts. This leads to troublesome, annoying and slow apps that cease to be efficient due to unnecessary and irrelevant functionality.

The concept of micro app revolves around two essential fundamentals:

  • Quick, targeted, narrow, get in – interact – get out function, incorporated into a single mobile app.
  • Performing tasks in real time, irrespective of whether it is personal or professional.

In comparison to the usual enterprise mobile apps, micro apps offer an experience that is more specific and is easy-to-use.

Organizations are constantly looking for new ways to increase employee efficiency, but often only further perpetuate the problem by introducing yet another new app or program. Reaching employees through the most actively used channel throughout the day is the key. Choosing the right mobile technologies to support higher levels of employee engagement will build stronger ties regardless of location.

Micro apps have emerged as a way to take some of the pressure off and help businesses push past the barriers of enterprise mobility. Just as mobile apps are transforming the way enterprises go mobile, micro apps are transforming the way people work.

Micro apps are simpler and more targeted, allowing employees to quickly perform specific tasks from any device, email, browser, or communications client. They provide highly focused, task-based functionality that let users get in, interact, and get out with maximum efficiency.

Instead of spending months developing a huge all-in-one mobile app, organizations can quickly create micro apps that address a specific employee need or streamline a single business process, such as approving an expense report or checking on the status of a lead in a CRM.

Engagement, productivity, and retention are directly related to the measurement of an employee’s satisfaction in the workplace. When employees are engaged they are more likely to be motivated to perform and stay where they are. Select the best mobile technologies your organization needs to reach the performance levels. The better the selection of tools with the right content, the more autonomous employees will be at making the decisions they need to perform and the better will be the clarity in communication among the employees.

With Fusion Informatics, companies can get an edge over their competitors by developing micro apps for better employees’ productivity, adapt to changing needs and priorities, and focus their time on growth.

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Machine Learning, the New Business Intelligence:

It’s 2018, there’s no doubt about the fact that the world is aware of the wonders one can of do with Artificial Intelligence and Machine Learning.We are living in exciting and innovative times with futuristic technology literally at our fingertips.

How machine learning can help ones business is the question that is on every entrepreneur’s mind. We’ve got to the stage where we take intelligent machines for granted. We trust them to carry out complex tasks requiring extreme precision, speed, and accuracy – from pacemakers to auto-pilots. But while autonomous cars get all the headlines, it’s actually many other industries that may feel the greatest impact of the new tech. So,it’s very intriguing to learn howmachine learning improve businesses intelligence.

Put simply, machine learning is about understanding data and statistics. It’s a technical process where computer algorithms find patterns in data, then perform functions – like improving the core functionality of existing software and analytic, uncovering previously inaccessible insights hidden in large data sets unstructured data formats, and taking over tasks like image recognition, text analysis, and repetitive knowledge work. The potential use cases are seemingly endless, from supply chain and risk detection to logistics and technical support to behavioural analysis and customer support.

Businesses can also use machine learning to up-sell the right product, to the right customer, at the right time. In 2018, marketers will continue to rely on machine learning to understand open rates when it comes to email– so you know exactly when to send your next campaign.

Retail companies are also tracking what ads or images you’re most likely to stop scrolling on, in order to target you with specific content. Machine learning is applied to reduce the risk of credit fraud in small businesses. Machines learn from historical datasets that contain fraudulent transactions and can identify patterns that represent a typical fraudulent transaction.

The value that machine learning can deliver will be dependent on the degree to which these systems can deal with structured and unstructured data and its quality. Similarly, Chatbots can also help achieve a faster customer service resolution, as well as provide quick histories of each customer for impeccable customer service.

Consequently, machines will not eliminate human intervention in decision making. Machines will simply change the nature and timing of our intervention. A machine self-learns so that the algorithms can predict accurately. However, final decisions have to be taken by living, breathing, empathetic entities.

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Trends Transforming Cloud Computing in Year 2018

Cloud computing accelerates Enterprise Transformation everywhere

Cloud is no longer about cheap servers or storage — it’s now the best way to turn great ideas into amazing software even faster. In 2018, cloud computing will accelerate enterprise transformation as it becomes a must-have business technology.

After gathering, analyzing and prioritizing we have listed The Top 5 Trends in cloud computing that you should be aware of in 2018:

# 1- Internet of things on cloud

Most IoT devices are heavily dependent on cloud solutions, mainly with connected devices working together. IoT based devices such as electronic appliances, cars, digital security systems, and trackers have a cloud-based back end as a means to communicate and store information. Cloud supports these devices, and as we see a rise in IoT devices being manufactured and sold, the cloud usage will continue to increase as a result.

# 2- Bigger cloud storage capacity:

Large scale businesses with tons and tons of data will require more space to store that data. Cloud service providers in 2018 will bring in more data centres online with larger-capacity storage. According to the Cisco survey, the total amount of data held in data centers will stand at 370 EB. Conversely, global storage capacity would reach 600 EB. We should expect these numbers to increase to about 1.1 ZB worth of storage capacity. Businesses with big data will choose increased space options whereas small businesses can have bespoke storage options at lower prices as compared to 2017.

# 3- Penetration of high internet speeds

The amount of data is exponentially growing and therefore consumers are expecting faster and better internet connectivity from their network service providers to load website pages and apps quickly. The year 2018 will witness strong movement from gigabyte LTE speeds to full 5G networks, helping us reach 5G capabilities in record time. Many businesses will make a move by upgrading their SaaS, PaaS, and website platforms to be more responsive.

# 4- Artificial Intelligence revolutionizing cloud computing

Artificial intelligence and machine learning have already made an impact on the cloud computing. Technology giants like Google, Microsoft, Apple, IBM etc. have been heavily investing on these techniques for their business growth and transformation. Reports suggest that the investments made my tech-giants will contribute in a huge way for revolutionizing the future of cloud computing.

#5- Cloud Security

Cloud services from managed security service providers will be on demand. The businesses that may not be able to implement completely on security measures can easily rely on cloud service providers offering robust services.

Even after robust security options, year 2017 has witnessed the worst cyber attacks such as WannaCry, CIA Vault 7, etc. Security experts and analysts strongly feel there is a need to pay more attention to cloud security in order to combat cyber attacks.

At Fusion Informatics, we deliver cost-effective cloud based solutions to our clients.

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Machine Learning a Catalyst for Enterprise Productivity

What is that one thing that every enterprise strives to achieve?

It’s “Productivity”.

With the advancements in the technology, it’s quite evident that automation and artificial intelligence will continue to affect enterprise’ growth in a big way. Machine learning which fall under the umbrella of artificial intelligence (AI), has advanced rapidly over the decade and has become more accessible for enterprises to adopt. It won’t be wrong to say that machine learning will dramatically change the way enterprises do business.

The fascinating part about Machine learning is the fact that machines can learn features freely, precisely and in most of the cases large quantities of data can be introduced to ML Model which can prompt them to take independent decisions.

As computer machines are increasingly becoming smarter and capable of self learning, reasoning and determining the best course of action in real time, enterprises are poised to gain sustainable competitive advantage but researchers consider that Machine learning is still at nascent stage and its adoption among SME’s will take a little more time than expected.

So the million dollar question that keeps ticking in our mind is “In near future, will the evolution of Machine Learning replace human intervention in enterprises for decision making”? The answer to this question is quite tricky. Many experts believe that business decisions are not merely about evaluating options and choosing one but these decisions also require ethics and consideration of other intangibles that humans are accounted for. A balance can be created between humans and machines (AI) where humans can instruct computer to evaluate options and suggest for best possible outcomes for making a decision. This type of cooperation can take any enterprise to the next level of productivity.

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IoT A Giant Leap For Mankind

The internet landscape is growing at an exponential rate. It is not just limited to desktops, laptops, mobile tablets and smartphones anymore. There are now a multitude of devices that are connected to internet. IoT finds applications in several fields, which includes manufacturing, transport, biotechnology, home living and every aspect of our daily lives. We have witnessed it disrupting traditional industries, transforming businesses and most importantly changing the way we consume internet. Infact, according to a report from the Business Insider, which predicts that by 2020, there will be 34 billion devices connected to the Internet

Now, to give you a glimpse of how applications of IoT will make a difference in our lives, we have listed down few areas where IoT is eagerly awaited and technology companies like us are gearing ourselves up to surprise you with smart devices.

Smart Homes

Imagine a home where appliances can be controlled and managed through one tap of your smartphone or even through your voice command. All this is possible with IoT based smart home solutions.

Smart home solutions will bring a radical change in our lifestyle. It will certainly revolutionise the concept of residential and commercial properties and will substantially bring down the cost of living in the shape of efficient management of utility services within the apartments and enhance the safety and security quotient.

Smart Wearables

Wearing smart watches and fitness bands are the hottest trends in today’s day and age which is why technological giants are investing heavily on these devices.

These Smart wearable devices comes with high quality, small sized energy efficient sensors and pre-installed softwares which can keep a count of all your day to day activities. The data collected is further processed to carry out valuable insights. These devices broadly cover health, fitness, communication and entertainment requirements.

Smart Businesses

Another sector for the utility of Internet of Things is led by private and government organisations and that’s where savings and efficiencies will be made.

For businesses, IoT is a way to to improve customer satisfaction, quality and reduce operational costs while optimising time, staff and assets. Its is predicted that internet of things will certainly become a game changer in the technological environment, for businesses.

Smart Agriculture

With a rapid increase in world’s population, demand for supply of food has drastically gone up which is a point of major concern for the governments across the globe. This is the place where IoT will play a vital role to resolve global food crises.

Smart farming is one of the fastest growing areas in IoT where farmers use meaningful insights from the captured data which ultimately helps them to yield better ROI. Sensing soil fertility and determining inputs for better production is one among the work that IoT can do for farmers.

The future of IoT is highly captivating and is not limited to few areas only, but there is much more to it. It won’t be wrong to say that it a giant step towards mankind. There are several other areas which will get benefitted through IoT in a huge way. We shall cover those in our upcoming blogs.

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Biomimicry and Robomimicry for AI Self-Driving Cars: Machine Learning from Nature

By Dr. Lance Eliot, the AI Trends Insider
The fish in the aquarium tank were going round and round the inner edges of the glass surface that encased them in water. Humans watching the fish were likely wondering whether or not the fish knew they were in water. There is an ongoing philosophical debate about whether or not fish can comprehend that they are immersed in water. Maybe they take it for granted just like we take for granted that we are surrounded by air. Maybe they are actually deep thinkers and know they are in water and that they must stay in water to survive. Or, maybe they have no capacity to think per se and so the question of whether they are in water is not even something that they can entertain.
Regardless of the broader question about whether fish know they are in water, the humans watching the fish were looking for something else. The humans were researchers that wanted to see if the fish would demonstrate certain kinds of behaviors. For you see, the experimenters had created a robotic version of a fish and were waiting eagerly to see if the regular fish would accept the robo-fish as one of their own. If the living fish swam along with the robo-fish, it would tend to imply that the real fish were not frightened or otherwise taken aback by the robo-fish.
Indeed, the fish were swimming right along with the robo-fish, all of them going round and round in the tank. Success for robo-fish!
The researchers were wondering too whether the robo-fish could even get the fish to change their behavior, somewhat, such as convincing the fish to follow the direction of the robo-fish. Up until now, the robo-fish had been swimming in the same direction as the fish.  This seems to suggest that the fish had accepted that the robo-fish was safe to be near. Would they also though be willing to change their own behavior and end-up following the robo-fish. In some mild respects, yes, it turns out that the fish would follow-up along with the robo-fish.  More success for robo-fish!
Now, don’t go too far on this. It’s not as though the robo-fish got the living fish to do the macarena macaroni dance in the middle of the water tank. Instead, it was more akin to slightly altering the direction they were already headed and so a very modest impact on their behavior. But, who knows, maybe one day we’ll be creating robo-fish that are the king of the fishes. All fish hail to the robo-fish! It could become a takeover of all fish by the robots, which presumably (hopefully!) are being controlled by the humans. So, humans control the robo-fish, which in turn control the fish. I know this might seem quite untoward and maniacal. Maybe another version of the future is that robo-fish will live in harmony with regular fish, and they will all help each other. Robo-fish and regular fish will become blood brothers, though I guess without the blood part of it.
One of the research studies about robo-fish that caught my attention involves the study of zebrafish and the development of a modular robotic system that mimics this small fish’s locomotion and body movements. The work is being done at the Robotic Systems Lab in the School of Engineering at the Ecole Polytechnique Federale de Lausanne in Switzerland, and with the Paris Interdisciplinary Energy Research Institute at the University Paris Diderot.  
Let me point out that trying to create a robot that is as small as a zebrafish and that has the same motion pattern and look as a zebrafish is a hard problem. The system is known as the Fish Control Actuator Sensor Unit or Fish-CASU, and it attempts to not only look like a zebrafish but also aims to swim at the same linear speed and acceleration as the real fish. There are two main components, the FishBot and the RiBot, and it uses the popular Raspberry Pi processor along with a computer that communicates via Bluetooth and infrared with the robo-fish.
By first carefully studying the zebrafish, the researchers were able to determine that the fish follow a particular sequence while moving in the tank. The first step involves the zebrafish gaining their orientation and they do caudal peduncle bending to start their propulsion. Next, the fish go into a high linear acceleration mode. Third, in the relaxation step, they stop their tail beating and begin to glide in the water, gradually their linear speed decreases during this step.  Generally, the zebrafish then repeat those three steps, over and over. The researchers opted to develop a finite-state machine that would get the robo-fish to do roughly the same, namely the orientation, acceleration, and then relaxation steps.
The idea of building machines that mimic the behavior of animals is of course a notion that has been with us for a very long time. Biomimicry is the study and attempt at trying to mimic the behavior of biological creatures. If you look at the work of Leonardo da Vinci, you can see that he was fascinated by birds and hoped to someday develop a machine that would allow man to fly like birds do. Even the Wright Brothers likewise used biomimicry to help get mankind off the ground and flying into the air.
As they say, imitation is the highest form of flattery. If animals can do something, perhaps we can create machines to do the same.
One twist to this topic involves the aspect of potentially changing the behavior of the mimicked creature. In other words, it’s one thing for us to be able to fly in airplanes, and another to have us use biomimicry inspired robots to change the behavior of the birds. Suppose we created a robo-eagle and had it fly along with eagles. Maybe the robo-eagle could warn real eagles when a hunter was trying to shoot at the eagles, or maybe keep the eagles from running into the wall of a building or into the engine of a jet plane. You could say that the biomimicry could be used for purposes of good, augmenting the true creatures and aiding them. As with anything that involves good, there’s the chances too of the bad, such as maybe using the robo-eagle to lure the eagles into a trap of some kind and lead to their destruction or extinction.
Anyway, the overall point is that we can study living creatures and try to create robo-like versions of them and then use those robo-versions to possibly change the behaviors of the creatures themselves. The part in which we try to create robo-like versions is what I call biomimicry. The part about using the robo-like version to then change the behavior of the living creature I call robomimicry. In essence, the living thing begins to mimic the robot thing.
What does this have to do with AI self-driving cars?
At the Cybernetic Self-Driving Car Institute, we are using the techniques of biomimicry and robomimicry to understand and enhance the AI of self-driving cars. This will be important along the path toward achieving true self-driving cars, those that are at the level 5. A level 5 self-driving car is one that can drive the car in whatever manner a human could drive the car. To-date, we’ve seen mainly level 2 and level 3 self-driving cars, and some auto makers and tech firms are just getting to the edges of level 4. We still have a long ways to go before we get to a true level 5.
From a biomimicry perspective, you could say that we are already trying to mimic the biological creatures that underlie cars, namely the human drivers. I realize this seems a bit odd in that usually you think of biomimicry as trying to mimic perhaps a horse, or a bird, or fish. In the case of cars, cars are already a type of machine, but there is a biological component essential to that machine, which is the human that drives the machine.  Therefore, it makes sense that we would want to mimic the human driver when trying to create a “robot” that can do the same thing (an AI self-driving car).
Allow me a moment to give an example of how biomimicry can be subtly but demonstrably applied.
Recently, the Nissan 2018 Rogue SL AWD was released. The car has a limited version of self-driving capabilities, including the ProPilot smart adaptive cruise control. As akin to similar systems on other auto makers cars, it allows the system to steer and drive the car while in a constrained highway driving situation. The human driver must still remain attentive to the driving task. The driver’s hands are to remain on the steering wheel, and the system the prompts the driver to periodically nudge the steering wheel to prove that they (the human driver) presumably are still paying attention to the road.  Similar kinds of adaptive cruise controls are found on the Tesla Autopilot, the Mercedes Benz DistronicPlus, and the Cadillac SuperCurise.
In the case of the ProPilot, it often appears to move back-and-forth within the lane. It veers toward the leftmost part of the lane, and then corrects itself toward the center, and then tends to veer toward the rightmost part of the lane. Many would not notice the car doing this. It takes a keen eye and an awareness of driving behaviors to readily realize this aspect. In some respects, this would be the same as a novice driver, imagine a student learning to drive. They over-correct in one direction and then the next. The ProPilot also tended to at times brake sharply in traffic, seemingly as though it was belated in recognizing that it was time to apply the brakes. The acceleration would do the same, at times jerking forward and rapidly accelerating when a more gradual increase in speed would do.
A human driver that is a novice might do all of those things. They would be over-correcting within a lane and tend to “weave” rather than be able to keep a steady center-lane approach. They would tend to brake suddenly rather than gradually. They would tend to accelerate rapidly rather than gradually. A more seasoned and experienced driver would be able to generally keep to the center of the lane. They would be able to gauge when to apply the brakes and do so without a sense of dramatics to it. They would be able to accelerate in a smooth manner that would not have the occupants in the car feel like they are in a rocket that is zooming into outer space.
This behavior of the ProPilot could be enhanced by using biomimicry of human drivers, particularly seasoned human drivers. The smoother version of driving is what the self-driving car should attempt to achieve. The odds are that the ProPilot was programmed to consider the angles and torque and other driving factors to mathematically calculate what to do. By also then seeing how human drivers drive, the self-driving capability can become more like human drivers.  This is one of the advantages of using machine learning as part of the AI development for self-driving cars. Machine learning based on large data sets of human driving are able to “mimic” the human driving behavior, even if the system itself does not necessarily have any logical reason for it per se, and instead it uses often neural networks which mainly try to find a pattern and mimic to that pattern.
Improvements in AI self-driving cars will occur as the AI becomes more biomimetic of how humans drive.
There is an additional twist to this. Right now, the biomimicry is based on how humans drive today. But, keep in mind that once AI self-driving cars become more prevalent on the roadways, we are likely to see a change in the driving behavior of humans.
Say, what?
Yes, we will begin to see human drivers changing their behavior due to the behaviors of the AI self-driving cars. In a sense, we’ll see robomimicry.
Let’s first look at what is going to happen as AI self-driving cars become somewhat common place on our roadways.
Here’s the human reaction:

      Awe
      Wide Berth
      Acceptance
      Treat Like Second-Class Citizen
      Begin to Ignore or Disdain

At first, human drivers in their cars will tend to look at the AI self-driving cars in awe. Look, there goes a self-driving car! Let’s follow it to see what it goes. Oh my gosh, did you see it come up to that red light, it made a perfectly good stop at the red light. And so on.
Most of the human drivers will opt to give a wide berth to the self-driving car. It will be the same kind of reaction that seasoned human drivers give to novice drivers. When you see a human driven car that has a sign “Student Driver” you usually give that car a wide berth. You figure that the human driver might do something untoward and will likely be driving in a very timid way. So, you switch lanes to go around it, or you give it extra distance from your car. Human drivers will tend to do the same with the first round of AI self-driving cars.
Gradually, we’ll begin to see acceptance of the self-driving cars. They will be gradually improving in their AI driving capabilities. Rather than giving them a wide berth, instead we’ll see a lot of human drivers that have lost the awe aspect, and instead are irritated or frustrated at the self-driving cars. Why is that darned self-driving car going so slowly? Why is it waiting so long at the stop sign? Human drivers will begin to see the self-driving car as a kind of second-class citizen.
We’ll begin to see human drivers trying to trick or exploit the AI of the self-driving car.
Imagine these kinds of human driving behavior:
I know that the self-driving car waits a long time to make a right on a red light, so I’ll swing around the self-driving car and sneak in front of it, allowing me (as a human) to make the turn without having to wait for the AI self-driving car to do so.
I’ll outrace the self-driving car since I am willing to zip through a yellow light while the self-driving cars are all being cautious and coming to a halt as soon as they see the yellow light and don’t want to race through an intersection.
Up ahead there is a self-driving car, and I can use it to block traffic for me, by getting in front of it, it will try to maintain the proper driving distance and I can then exploit it to prevent traffic from catching up with me.
These are examples of how human driving behavior will change, due to the introduction of AI self-driving cars. Those examples tend toward workarounds regarding the AI self-driving cars. We might say that those human driving behavior changes are “bad” because they are tending toward worse driving behavior by the humans.
Oddly enough, there is a chance that the changes in human driving behavior will be for the good. The robomimicry of human drivers mimicking the self-driving cars could actually get human drivers to be better drivers. If the AI self-driving cars are all tending toward the proper driving distances on the highways, it might get the human drivers to do likewise. If the AI self-driving cars exhibit minimal lane changes and it leads to faster traffic flow, perhaps human drivers will do the same. Whether the human drivers will do this because they mentally see the connection between how the AI self-driving cars are driving and their own driving behavior is an open question. It could be that the human drivers will just witness what is going on and tend to follow along, rather than overtly opting to drive differently.
Not all human drivers will be driving the same way. Some human drivers will more quickly adapt to the AI self-driving cars, while others will take longer to do so. Some human drivers will try to exploit the AI self-driving cars, while others won’t. It will be a mix. Overall though, we need to realize that the introduction of AI self-driving cars onto the roadways will have an impact on human drivers. Currently, most researchers and auto makers are assuming people will drive as they do. It is assumed that the driving behavior of humans is static. The reality is that human driving behavior is dynamic. Humans will change as they see other facets of the roadways and how AI self-driving cars are driving. Biomimicry leads to robomimicry, which will lead to more biomimicry, and so on.
What will human drivers think of AI self-driving cars that eventually can drive as well and perhaps even better than humans?  It reminds me of this famous quote by Immanuel Kant: “Even a man’s exact imitation of the song of the nightingale displeases us when we discover that it is a mimicry, and not the nightingale.”
This content is originally posted on AI Trends.
Source: AI Trends