IoT vs AI: Difference Between Internet of Things and Artificial Intelligence

Internet of Things (IoT) and Artificial Intelligence (AI) are currently topping the charts as one of the most trending tech topics of the decade possibly. The two concepts have a different baseline working altogether, but many times when they are used together they are considered as an epitome of innovation in the Tech Industry.

IoT can leverage the power of AI and help develop interesting use cases which can help Tech Giants boost their Innovation and Research aspects and help sustain their businesses for a longer period in the foreseeable future. Let us dive into what IoT and AI are, their differences and future.

IoT vs AI: Definition

What is Internet of Things (IoT)?

IoT as the name suggests are devices/appliances which are connected to the internet. We can see a wider and more consistent availability of the Internet around us in 2020. Especially after Covid-19 struck, the Internet has officially entered the essentials to survive category for a majority of the population.

IoT harnesses this exact power of the internet to make things smart. Anything ranging from a Tesla car to smart home products (AC, Fridge) to Industrial Equipment and Machines which are connected to the Internet fall in the category of IoT devices. These units are continuously connected to a cloud server which can perform tasks like:

  1. Remotely updating softwares
  2. Collect Sensor data, performance data
  3. Remotely control the devices (sending task instructions)

One of the oldest yet famous experiments of IoT was in 1982 when the C.S. grad students of Carnegie Melon University connected a Coca Cola vending machine to the internet. The program coded used to return the temperature of the drinks and check the availability of the same.

The later function is known as inventory tracking and management, it is a major application IoT in the industry currently. One of the major advantages of IoT is many devices can be connected to the same host, doing so these devices can share the data with each other. In easier words, one device can talk to the other.

For example, if someone is using a smart lock in the main door, and they return back home. The smart lock can alert the lights and AC in the hall/room and they’ll be automatically switched on. This although is a very basic example, can be scaled up to develop even more complex relationships between devices.

Top industries which receive high amounts in IoT spending are Discrete Manufacturing, Transport and Logistics, Utilities, B2C and Health-Care. The projected spending by the end of 2020 is € 250 Billion according to Forbes.

Read: Machine Learning Models Explained

What is Artificial Intelligence (AI)?

As the name suggests, Artificial Intelligence is the Intelligence demonstrated by machines. The very common notion associated with AI is that when a machine performs or takes decisions in a human-like way. The term AI goes back to 1956 when the term was officially coined. The growth of AI was very nominal early on. Recent advancements in computing powers gave a major boost to AI.

AI generally has two components to it. One is the rule based component which can be achieved by simply writing logics and programs. The true intelligence part comes into picture with the introduction of Machine Learning and Deep Learning techniques. This is the part which resonates with Machines having inherent intelligence.

When we try to understand how a human being learns, it is because of a series of similar events which end up making humans learn. For example, when someone wants to learn a language, he/she constantly practices it repeatedly. Taking inspiration from the same learning process, major Machine Learning Algorithms are created.

For these algorithms, the series of events are actually in the form of Data. Human kind has seen an exponential growth in data in the last few years. This data fuels the intelligence that drives the AI industry in current times. The higher the quality of data, the better trends and patterns can be extracted from it. Hence enhancing the learning and prediction abilities of any AI system.

AI has its application in various industries like Finance, Human-Resource, Health-care, BFSI, E-commerce. Data heavy industries definitely have an upper hand in leveraging the power of AI compared to others. Many companies are investing heavily in AI and the future looks very promising. The current estimated worldwide spending on AI by the end of 2020 is 50.1 Billion dollars and estimated to be doubled by 2024.

IoT vs AI: Comparisons

Cloud Computing

AI strongly utilises Cloud Computing capabilities. Cloud computing platforms really help facilitate AI projects in a more easy manner. Complementing the same, the data generated from IoT devices is easily communicated over the cloud and various AI analytics processes can be applied on them. Cloud computing increases the efficiency of AI and IoT and also provides a harness for interoperability

Costs

IoT projects generally incur costs related to hardware, wireless connectivity, host server (if applicable) and respective software development. Whereas costs related to AI projects are generally related to Data gathering, Data Lakes/ Data Warehouse, Model Deployment and Software Development. IoT projects are generally less costly compared to AI projects.

Success Rates

AI projects generally have a lesser success rate compared to IoT. According to a survey by IDC, the highest success rates figures for AI were reported by just 30% of the companies. For the rest the failure rate ranged from 10% to 49%.

There are various reasons why AI projects fail one of the biggest of them all is lack of data (quality and quantity). IoT projects might face component failures but overall mostly are successful.

Also read about: Machine Learning Engineer Salary in India

Scalability

IoT projects are easier to scale because of the existing cloud based structure. Although there are many factors like architecture design, speed, etc. which can affect the scalability of any project. But if any IoT project is implemented keeping Scalability in mind it is easier to scale.

Whereas there are many variables which makes it a little difficult for AI projects to be scaled. But again if the design is more flexible and modular, that helps in easier scalability.

IoT and AI: IoT Analytics

IoT analytics is the field where AI and IoT come together. The data generated by IoT systems can be used by AI based models for Predictive and Inferential Analysis. IoT Analytics is one of the major applications of Data Analytics.

One very basic example of IoT Analytics can be the development of a model from a machine’s sensor data to predict its life. This can help give better insights on how often should the servicing be done and how servicing machines more or less frequently can impact the overall life of any component of that machine. Hence IoT analytics is the field where the abilities of both the domains are integrated together.

Conclusion

IoT and AI both have very shining and big futures. Individually and collectively as well. As the computing power, Internet and data availability increases, it will directly correlate to the growth of the respective technologies and their implementations in the industry.

There are various differences and similarities in the functioning of both technologies. But these are very potentially impactful if the power of both the technologies are correctly utilised.

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