AI is Beating the Hype With Stunning Growth

Follow the money. It is true in politics, business, and investing.

Gartner, a global IT research and advisory company, surveyed 3,000 CIOs operating in 89 countries in January. The Stamford, Conn., firm found that AI implementations grew 37% during 2018, and 270% over the last four years.
This is a trend investors should embrace. That’s because it is going to last for a while. And it’s going to make a lot of people very rich.
Investors have soured on AI recently. Self-driving cars, smart cities, and robotics keep getting smacked down as idealistic hype. That’s mostly because their implementations are decades away … or that these ideas are expensive solutions looking for problems.
So say the critics, anyway.
They point to once-high-flying stocks like Nvidia, which just saw its share price get cut in half because of slowing demand for cutting-edge gear and software.
However, that assessment is lazy. It also misses the point.
AI is a digital transformation story. Corporate managers realize AI software can help automate large parts of the enterprise, increasing productivity and saving a boatload of money.
It is true that machines will not be able to wholly replace complex human decision-making anytime soon. But the software is more than sufficient to processes mundane, repetitive tasks. And machine learning, a type of data science, can help humans see important patterns they might otherwise miss.
So companies are going all-in.
They are deploying software bots online, along with customer-relationship software to help service reps assist customers.
Executives are using integrated suites and data analytics to manage projects, workflows, payrolls and human resources.
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Devices in IOT

Components of IoT

IoT is becoming the trend of nowadays. Sooner or later it will not just take over the industrial sector but also impact the daily household. When designing an IoT ecosystem, there are certain factors which we need to bear in mind. Security, devices to be used i.e. sensors, microcontroller, gateways and cloud computing. The devices or “things” play a vital role. Right from a refrigerator door to a coffee machine; will be connected to the Internet.  With rapid advancement in technology and 5G’s demand in the future, it is believed by the year 2021, approximately 20 billion devices will be connected to the internet.

What is IoT basically? A network where not only a vehicle, a home appliance like fan or air conditioner but also embedded systems like electronics, software, sensors which enable exchange or transfer of data over wireless technologies like Wi-Fi, ZigBee, Bluetooth etc. IoT in short will reduce human effort and increase our dependency on machines.

In IoT, the major components are Sensors, Gateway or microcontrollers, connectivity, analytics or data processing and cloud computing.


iot blog1

  • Sensors: They are the “things” in an IoT system. They are responsible for collecting and transmitting real-time data to the microcontroller. Sensors are used to detect physical changes for example, temperature, humidity or pressure. Here are some features which should be the basis of a good sensor:
  • It should not be sensitive to the phenomena it measures
  • It should not detect other physical changes apart from its designation for example, DHT11 is designed to sense the temperature and humidity of its surroundings. If it starts determining the luminosity then its problem which needs attention.
  • It should not modify the readings during the measurement process.


There are several properties or characteristics one should keep in mind while selecting a sensor:


Characteristics of a sensor
Response and Recovery Time


  • Microcontrollers or Gateways as they are commonly known. Like in a human body the brain controls actions and movements, similarly microcontroller acts as the brain of an IoT system.


Why should we use a microcontroller in an IoT system?

  • Simplicity: Programming a microcontroller or a setting up is no difficult job. Also, interfacing sensors with a microcontroller is easy.
  • Security: Code is written on a “bare metal” in a microcontroller which results in very little attack and maintains a secure environment.
  • Cost: Microcontroller are very cost-effective. With minimum cost they offer you simplicity and security.

Raspberry Pi, Arduino Uno, NodeMCU are a few examples of microcontrollers.

The factors one should keep in mind while selecting a Microcontroller:

  1. Compatibility: Will our Microcontroller support the sensors/ actuators? Depending upon how sensors are being, you should decide the number ports required.
  2. Architecture: If your Microcontroller will be able to handle complex programming, what are the functional requirement of your application and also the power it should compute for the application to run.
  • Memory: Choosing the microcontroller with enough memory size is of utmost importance, in order to save time and money.
  1. Availability: A thorough research is a must about the availability of the microcontroller and the quantity. Selecting a correct microcontroller during the initial stages of your project is important which can help scale your application.
  2. Power: Energy efficiency plays a key role in designing an IoT system. How much power does it require, will it need to be wired or whether batteries are required.


Hence, this component of the IoT needs to be the most secured part as it analyses, processes data from thousands of sensors and acts a gateway to the cloud. Microcontroller (MCU) should also have the capability to host the process, store data i.e. act like a memory and provide a secured operating system.


  • Analytics or data processing or Data Analytics play another significant role in an IoT system. Drawing conclusions from big or small data is basically data analytics. It will play an integral part in the development of IoT. The following points enlist the effect it will have on the businesses.


Volume: The shear amount of data to analysed will be huge in an IoT application. Real-time data from many sensors or actuators will require data analytics.


Structure: The data coming from sensors will be structured, semi-structured or unstructured. This will require data to be analysed on a bigger scale.


Revenues: Data analytics will help us provide an insight of what the customer demands and expects from the analytics. This will increase or generate revenues for the same.


Competition: As we know IoT is going to be the future. It provides freedom and better performance. Hence, by offering data analytics, one can upgrade his or her business.



  • Cloud computing: If Microcontroller is the brain of an IoT system then Internet is the heart of an IoT system. There are many ways to connect to the Internet for example Wi-Fi, Bluetooth. Cloud computing is a sector vital in the evolution of IoT. If IoT provides the data then cloud provides the path for the data to travel.


Cloud computing’s sole motto is to enable IoT or users to access data from remote parts of the world through its storage option. Also, for the developers to work from different parts of the world. Cloud computing is also economically viable as it has minimum pay charges which depends on the cloud model. For example, Microsoft Azure Cloud Services, in their free trial provide upto 8,000 messages per day. This will encourage IoT companies or start-ups and in turn reduce costs.


Some of the cloud computing platforms are:








Book: Internet of Things by Dr. Jeeva Jose








Improving Verifiability in AI Development


We’ve contributed to a multi-stakeholder report by 58 co-authors at 30 organizations, including the Centre for the Future of IntelligenceMilaSchwartz Reisman Institute for Technology and SocietyCenter for Advanced Study in the Behavioral Sciences, and Center for Security and Emerging Technologies. This report describes 10 mechanisms to improve the verifiability of claims made about AI systems. Developers can use these tools to provide evidence that AI systems are safe, secure, fair, or privacy-preserving. Users, policymakers, and civil society can use these tools to evaluate AI development processes.


While a growing number of organizations have articulated ethics principles to guide their AI development process, it can be difficult for those outside of an organization to verify whether the organization’s AI systems reflect those principles in practice. This ambiguity makes it harder for stakeholders such as users, policymakers, and civil society to scrutinize AI developers’ claims about properties of AI systems and could fuel competitive corner-cutting, increasing social risks and harms. The report describes existing and potential mechanisms that can help stakeholders grapple with questions like:

  • Can I (as a user) verify the claims made about the level of privacy protection guaranteed by a new AI system I’d like to use for machine translation of sensitive documents?
  • Can I (as a regulator) trace the steps that led to an accident caused by an autonomous vehicle? Against what standards should an autonomous vehicle company’s safety claims be compared?
  • Can I (as an academic) conduct impartial research on the risks associated with large-scale AI systems when I lack the computing resources of industry?
  • Can I (as an AI developer) verify that my competitors in a given area of AI development will follow best practices rather than cut corners to gain an advantage?

The 10 mechanisms highlighted in the report are listed below, along with recommendations aimed at advancing each one. (See the report for discussion of how these mechanisms support verifiable claims as well as relevant caveats about our findings.)

Institutional Mechanisms and Recommendations

  1. Third party auditing. A coalition of stakeholders should create a task force to research options for conducting and funding third party auditing of AI systems.
  2. Red teaming exercises. Organizations developing AI should run red teaming exercises to explore risks associated with systems they develop, and should share best practices and tools.
  3. Bias and safety bounties. AI developers should pilot bias and safety bounties for AI systems to strengthen incentives and processes for broad-based scrutiny of AI systems.
  4. Sharing of AI incidents. AI developers should share more information about AI incidents, including through collaborative channels.

Software Mechanisms and Recommendations

  1. Audit trails. Standard setting bodies should work with academia and industry to develop audit trail requirements for safety-critical applications of AI systems.
  2. Interpretability. Organizations developing AI and funding bodies should support research into the interpretability of AI systems, with a focus on supporting risk assessment and auditing.
  3. Privacy-preserving machine learning. AI developers should develop, share, and use suites of tools for privacy-preserving machine learning that include measures of performance against common standards.

Hardware Mechanisms and Recommendations

  1. Secure hardware for machine learning. Industry and academia should work together to develop hardware security features for AI accelerators or otherwise establish best practices for the use of secure hardware (including secure enclaves on commodity hardware) in machine learning contexts.
  2. High-precision compute measurement. One or more AI labs should estimate the computing power involved in a single project in great detail and report on lessons learned regarding the potential for wider adoption of such methods.
  3. Compute support for academia. Government funding bodies should substantially increase funding for computing power resources for researchers in academia, in order to improve the ability of those researchers to verify claims made by industry.