Artificial Intelligence| Importance of AI in daily life. Disadvantages of AI. AI as a future

                                                     Artificial Intelligence (AI)

                                                                                                                                                       Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks the typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry.    

                                                    Founder of AI

               John McCarthy (September 4, 1927 – October 24, 2011) was an American computer scientist and cognitive scientist. McCarthy was one of the founders of the discipline of artificial intelligence.

                                                     

Importance of AI in daily Life

There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives.

AI assists in every area of our lives, whether we’re trying to read our emails, get driving directions, get music or movie recommendations.

 artificial intelligence is used in day-to-day activities such as:

• Social media
• Digital Assistants
• Self-Driving And Parking Vehicles
• Email communications
• Web searching
• Stores and services
• Offline experience  
• Over the years AI has forayed into many walks of our life.                                   
• The automobile industry has been greatly revolutionized with the advent of AI. For example, Google's self driving car uses AI techniques to understand its surrounding and to drive accordingly. These smart automobiles aim to achieve zero accidents and ease traffic congestion. They eventually reduce the passenger's travel time. Transportation and logistics companies like Uber use AI algorithms to provide traffic updates, estimated time of arrival and fares to the user.
• E-commerce organizations like Amazon and Walmart use AI to understand customer purchase behavior and to subsequently make recommendations.
• Financial firms deploy AI techniques to mine for fraudulent transactions in order to keep your data and money safe.
• Digital assistants like Siri and Alexa understand your voice and questions to provide information or to execute tasks.
• Networking platforms like Facebook use AI to detect if there has been unauthorized access of your images.
• Companies make use of AI applications called chatbots to provide customer support. These chatbots provide wide range of services. They can resolve queries or book appointments for you.

The list is endless. AI applications cut time, save money and reduce user efforts. It improves productivity and lowers cost. To the manufacturer, it helps provide a product of high value and reach. It also produces new creative growth opportunities.

                                                            

The disadvantages

Job losses

There is little doubt that artificial intelligence will displace many low-skilled jobs. Arguably, robots have already taken many jobs on the assembly line – but now this could extend to new levels. Take, for example, the concept of driverless cars, which could displace the need to have millions of human drivers, from taxi drivers to chauffeurs, very quickly. Of course some would argue that artificial intelligence will create more wealth than it destroys – but there is genuine risk that this will not be distributed evenly, particularly during its early expansion.

Distribution of power

Artificial intelligence carries the risk, in the minds of some, of taking control away from humans – de-humanising actions in many ways. Nations that are in possession of artificial intelligence could theoretically kill humans without needing to pull a trigger.

Lack of judgement calls

Humans can take unique circumstances and judgement calls into account when they make their decisions, something that artificial intelligence may never be able to do. One example occurred in Sydney, Australia, in 2014 when a shooting drama in the downtown area prompted people to make numerous calls to Uber in an effort to escape the area. The result was that Uber’s ride rates surged based on its supply and demand algorithm – there was no consideration involved for the circumstances in which the riders found themselves.

                                                         AI as a Future

Advances in computing power

Deep learning relies on computing power to solve more complex problems. With current technology, learning may take too long time to be beneficial. Therefore, there is need for advances in computing power. With new computing technologies, companies can have AI models that can learn to solve more complex problems.

AI-enabled chips

Even the most advanced CPU may not improve the efficiency of an AI model by itself. To use AI in cases like computer vision, natural language processing, or speech recognition, companies need high-performance CPUs. AI-enabled chips become a solution to this challenge. These chips make CPUs “intelligent” for optimizing their tasks. As a result, CPUs can work for their duties individually and improve their efficiency. New AI technologies will require these chips to solve complicated tasks and perform them faster.

Companies like Facebook, Amazon, and Google are increasing their investments in AI-enabled chips. Below you can find a chart of global equity funding for AI-enabled chip startups.

Source: Financial Times

These chips will assist next-generation databases for faster query processing and predictive analytics. Industries like healthcare and automobile heavily rely on these chips for delivering intelligence.

Advances in GPUs

 Quantum computing

Traditional computer syparallel tasks. Thus, GPUs are widely used in AI models for efficient learning.

stems work with binary states; 0 and 1. However, quantum computing takes this to another level and works with quantum mechanics. This enables quantum systems to work with qubits, instead of bits. While bits consist of 0 and 1, qubits consist of 0, 1 and an additional state, which includes both at the same time. This additional state enables quantum computing to be open to new possibilities and provide faster computation for certain tasks. These tasks include neural network optimizations and digital approximations.

IBM states that it will be possible to build a quantum computer with 50-100 qubits in the next 10 years. When we consider that the 50-qubit quantum computer works faster than today’s best 500 supercomputers, there is significant potential for quantum computing to provide additional computing power.

Advances in data availability

This is a point that does not need to be explained in much detail. Data availability has been growing exponentially and is expected to continue to do so with increasing ubiquity of IoT devices.

Advances in algorithm design

While the capabilities of AI improve rapidly, the algorithms behind AI models will also evolve. The advancements in the algorithm designs will enable AI to work more efficiently and be available to more people with less amount of technical knowledge. Below you can find the prominent advancements in AI algorithm designs.

Explainable AI (XAI)

One of the main weak points of AI models is its complexity. Building and understanding an AI model requires a certain level of programming skills and, it costs time to digest the workflow of the model. As a result, companies usually benefit from the results of AI models without understanding their workflow.

To solve this challenge, Explainable AI makes these models understandable by anyone. XAI has three main goals:

• How the AI model affects developers and users
• How it affects data sources and results
• How inputs lead output

As an example, AI models will be able to diagnose diseases in the future. However, doctors also need to know how AI comes up with the diagnosis. With XAI, they can understand how AI makes its analysis and explain the situation to their patients accordingly.

Transfer learning

Transfer learning is a machine learning method that enables users to benefit from a previously used AI model for a different task. In several cases, it is clever to use this technique for the following reasons:

• Some AI models aren’t easy to train and can take weeks to work properly. When another task comes up, developers can choose to adopt this trained model, instead of creating a new one. This will save time for model training.
• There might not be enough data in some cases. Instead of working with a small amount of data, companies can use previously trained models for more accurate results.

As an example, an AI model that is well-trained to recognize different cars can also be used for trucks. Instead of starting from scratch, the insight gained from cars will be beneficial for trucks.

Reinforcement learning (RL)

Reinforcement learning is a subset of machine learning which aims AI agent to take action for maximizing its reward. Rather than traditional learning, RL doesn’t look for patterns to make predictions. It makes sequential decisions to maximize its reward and it learns by experience.

Today, the most common example of RL is Google’s DeepMind AlphaGo which has defeated the world’s number one Go player Ke Jie in two consecutive games. In the future, RL will also be available in fully automated factories and self-driving cars.

Advances in AI building tools

Though these are not novel algorithms, they can reduce the time to build models and enable both AI research and commercialization

Neural network compatibility and integration

Choosing the best neural network framework is a challenge for data scientists. As there are many AI tools in the market, it is important to choose the best AI tool for implementing the neural network framework. However, once a model is trained in one AI tool, it is hard to integrate the model into other frameworks.

To solve this problem, tech giants like Facebook, Microsoft, and Amazon are cooperating to build Open Neural Network Exchange (ONNX) to integrate trained neural network models across multiple frameworks. In the future, ONNX is expected to become an essential technology for the industry.

Automated machine learning

AutoML supports companies to solve complicated business cases. With this technology, analysts won’t need to go through manual machine learning training processes. They can even evolve new models that can handle future AI challenges. As a result, they will focus on the main case instead of wasting time for understanding the workflow.

AutoML also offers customization for different business cases. This enables flexible models when you combine data with portability. To learn more about AutoML, you can check our article.

What are the future technologies to be enabled by AI?

AI use cases will shape the development of AI. Availability of capital depends on use cases and more valuable use cases would motivate companies and government to invest more.

The improvement of AI will make our intelligent systems even more intelligent. Our cars will drive themselves, houses will adjust their electricity usage, and robots will be able to diagnose our illnesses. In other words, AI will cover more in our lives and will automate our daily tasks. Here are a few use cases of AI technologies that currently either exist in quite limited functionality or limited scope (research projects). Improvement of these technologies will unlock significant value.

• AI assistants
• AI-based medical diagnosis
• Autonomous payments
• Autonomous vehicles
• Bionic organs
• Conversational agents
• Smart cities
• Smart dust

Cloud computing based use cases

Cloud computing aims to create a system where you can achieve computing functions whenever you want. According to Gary Eastwood from IDG Contributor Network, cloud computing and AI will fuse in the future.

The integration of AI will help AI models to access information from the cloud, train themselves and applies new insights into the cloud. This enables other AI models to learn from these new insights. This fusion improves calculation power and the capability of treating many data and intelligence.

The possible use cases of cloud computing include AI-lead drones, sensor networks, and smart dust.