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05/12/2018

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This video to Show Graphite can pass current. We also know Pencil lead is made up of graphite. Be carefully while using pencil in circuit boards. Science stu...

02/04/2018

02/04/2018

“Hack your old phone to use it as a GMS modem for your next project. https://t.co/5ce7cZEVxA ”

18/03/2018

22/08/2017

control your robot using server
TECHNOLOGY

21/08/2017

, there is difference

Artificial intelligence and machine learning often crop up simultaneously in topics like big data and analytics. Both of them are often used interchangeably yet they differ from each other.

Artificial Intelligence on the one hand is defined as the intelligence exhibited by machines by which it mimics cognitive functions that humans use while interacting with other humans. On the other hand, machine learning means to provide data to machines and let them learn for themselves.

1. Machine Learning

Machine learning in one line can be defined as the ‘Algorithms through which machines can learn by using data, observations and experiences’. Powerful machine learning algorithms can help create various archetypes that can accurately predict future events and can warn people in advance. It can also help machines to make intelligent decisions based on their past experiences. Years of research in this field has helped to come up with algorithms that help machines to identify the problems and to come up with novel approaches to solve them. But still a great deal of code is written by researchers; hence years of research is still required to reach a level where machines can learn by themselves and come up with solutions without human help.

2. Artificial Intelligence

Artificial intelligence, in one line is ‘Human intelligence exhibited by a machine’. It is the ability of machines to inculcate the ability to think, to build perceptions, plan in advance and to respond like humans. The final aim of AI is to develop problem solving skills so that it can assist humans with their daily chores efficiently. Machine learning is a subset of AI because the ultimate goal is to make machines self dependent so as to take decisions based on logic, reasoning, rational thinking and past experiences.

The above points clearly depict the difference between machine learning and artificial intelligence where on one hand the former is about learning through data and past experiences and taking decisions on its basis; whereas the latter is about the ability of the machines to interact with humans and to help them in our day to day lives.

17/08/2017

IoT developers have taken the world by storm by creating and connecting millions of devices that now play an integral part in our lives. But this added utility comes with its own set of caveats and IoT challenges that need to be sorted.

IoT developers are constantly developing new and advanced devices for homes and offices that are connected with each other and the Internet. These devices are creating unprecedented opportunities in reducing energy consumption, cutting down costs, improving customer service, and building a sustainable approach towards our environment. But that’s just one side to the coin. The other side is the many challenges that glare at IoT developers.

4 challenges faced by IoT developers

Security challenges

IoT is already posing a serious threat to tech giants and government agencies all around the world. Smart fridges, cameras, and assault rifles are being hacked, casting an aura of fear with regards to the security and future of IoT. This problem is bound to escalate as IoT gets engrained more and more into our lives. Critical city infrastructure can be hacked, as in the case of the Ukraine power grid hack. In the rush to launch their product before their competitors, companies focus more on providing features than on the security. Moreover, most IoT developers have an embedded programming background, due to which they are ignorant about IoT programming and the threats related to it.

Privacy Challenges

IoT devices also collect sensitive data that is often protected by legislations like Health Insurance Portability and Accountability (HIPAA) in the U.S. However, special precautions needed for handling such information are amiss. Another thing to take into consideration is that while data generated by a single device may not be sensitive, it may reveal a lot of personal information when combined with data from other appliances.

Connectivity Challenges

One of the biggest challenges for IoT in the future is to connect large number of devices. Presently, we rely upon centralized, server/client model to authorize, authenticate, and connect several nodes present on the network. This model is sufficient for the number of IoT devices that are currently a part of the ecosystem. However, in the near future, when hundreds of billions of devices will join the network, it will turn into a bottleneck. Moreover, the capability of current cloud servers is so less that it can breakdown if it has to handle large amounts of information.

Compatibility and Longevity Challenges

Different technologies like ZigBee, Z-Wave, Wi-Fi, Bluetooth and, Bluetooth Low Energy (BTLE) are all battling to become the dominant transport mechanism between devices and hubs. This becomes a major source of problems when a lot of devices have to be connected; such dense connectivity requires the deployment of extra hardware and software. There are various other compatibility issues that are bound to stem due to the non-unified cloud services and lack of standardized M2M protocols.

Despite the immaturity of the Internet of Things (IoT) posing so many challenges, developers will not be deterred from chasing success in this area. IoT is an ecosystem of ever-increasing complexity; it is the next level to automate every object in our life. Since this is a relatively new area as compared to big data and cloud, and promises a bright future, developers will soon come up with solutions to the above stated problems.