This captivating exploration of AI's pervasive influence reveals many applications that have touched various facets of our lives. Embark with us on a journey through the extensive impact of AI as we delve into diverse sectors, uncovering the ingenious ways it has altered industries, shaped our realities, and laid the groundwork for a future brimming with possibilities.
In 2023, AI is already everywhere
Already in 2017, AI technologies have entered our everyday lives so firmly and deeply that more than half of their consumers didn't even realize that they are using AI technology daily.
Scientific advances in AI and computer vision drive our world forward. Already in February 2017, the social network Pinterest introduced the world to visually search for objects in images and even live from the camera. The idea behind this platform was to show the user items that might interest them.
Since 2017, when people come across something unfamiliar and interesting on their travels, they point a smartphone camera at this, and Pinterest gives all the details about the item. For example, the price, where to buy it, and whether you can touch it.
AI-powered natural language processing (NLP) technology is helping us expand our "language horizons". Learning apps like Duolingo add automatic speech recognition (ASR) to NLP to help users correct errors in the pronunciation of foreign words. Already in November 2017, Google released a pair of wireless headphones with real-time translation functionality. This solution allowed untrained individuals to listen to and speak 40 foreign languages simply by smartphone.
With AI, it is possible to run large-scale yet highly personalized advertising campaigns. Today, 4,8 billion people actively use social media. The unstructured content we create ourselves in posts, comments, videos, blogs, and forums is an advertiser's gold mine. Our moods, expectations, and preferences are available to them. All of this can now be analyzed in real-time, offering the products you and I need "here and now".
IBM Watson's Personality Insights tool can be used to create new and personally targeted services. For example, in the asset management industry, IBM Watson Investment Advisor can identify the relationship between a client's personality, life situation, and the vast ocean of financial market data. All of this data can be correlated with various investment alternatives to recommend the optimal strategy for managing a personal investment portfolio.
Corporate AI: does it means working harder or smarter?
Businesses in different industries often have similar challenges, problems, and needs. AI can improve the productivity and efficiency of virtually any company in the following areas:
- Customer support
- Research assistance
- Data entry
- Content research
Now let's discuss each of these areas and how AI influences them.
Customer support
Customer support can be improved by automating customer interactions using voice or chatbots. 48% of Americans say they’re comfortable engaging with chatbots or virtual agents when checking their financial account balances, while 30% say the same when they are purchasing an expensive item.
Continuous use of chatbots allows companies to improve over time and evolve to handle more complex requests. One example of this self-learning evolution was BlueBot, launched by KLM Airlines. At first, the bot could only answer the most basic queries. But now customers can book airline tickets through it and even get advice on "what to take with you on your trip". As a result, many KLM offices worldwide are saving a lot of money on call center staff, and their customers are getting the information they need much faster.
Research assistance
As a rule, creating any knowledge base is accompanied by painstaking research work. The average mental worker spends more than 1 hour daily searching for the required information in sometimes outdated systems, unstructured databases, and disparate intranets. Usually, the search is conducted by keywords. Such queries return results of poor quality, creating the need to generate repetitive iterations. AI can take over such searches using automatic clustering, ontologies, and visual recognition technologies.
Data entry
AI can automate generating of emails, invoices, spreadsheets, presentations, PDFs, and other documents using machine learning. Most companies need to process large amounts of information on a daily basis. For example, the same average person receives 100-120 daily emails. The fact that most documents today are digitized, of course, makes the task of preprocessing a little easier. But adding "a little" AI to the digitization process will significantly help office workers.
Content exploration
80% of any data is unstructured data. They are usually not analyzed by anyone. Already in 2017, over 17 trillion PDF files were in use across all enterprises worldwide. It is possible automatically analyze unstructured data contained in emails, PDFs, images, audio, and video. This is made possible by the development of Big Data analytics tools and, of course, AI.
AI in retail: online personalization, self-learning, and restocking
There will always be a place for artificial intelligence in e-commerce. Today, 49% of cart abandonments are due to high extra costs, including shipping, taxes, and fees. Several years ago, these people successfully returned to online stores with the help of retargeting ads. It was possible to reach up to 98% of them before they made a purchase in other stores.
But as the popularity of retargeting grew, this tool became more and more annoying. That's why many online shoppers don't respond to retargeting ads. This is partly because retargeting works on fairly simple direct scripts and cannot be truly personalized.
With access to billions of customer touchpoints in a store, retailers can run a neural network over that entire data set. To dynamically identify specific customer segments and recommend the most relevant products and services to each. For retailers who can succeed in this endeavor, an AI-based recommendation engine can be a powerful business tool.
For example, the video streaming service Netflix gets up to 80% of its views from an AI-based recommendation engine. Similarly, online retailer Amazon organizes product recommendations using AI. Purchases based on intelligent recommendations at Amazon are already 30% or more.
But not only online retailers are using artificial intelligence as their innovative weapon. Traditional retailers are actively experimenting with these technologies to improve their customer experience in physical stores.
US home improvement chain Lowe's uses an OSHbot developed with Fellow Robots to help customers find products they are interested in. OSHbots even walk with the customer to specific shelves and show them where a particular item lies. This implementation utilizes deep learning, natural language processing, and computer vision technologies. The latter is needed to recognize goods from large boxes to small bolts.
Most importantly, deep learning help to understand customer requests. If the robot cannot handle a request independently, it can engage a live consultant on the request. OSHbot also performs inventory management of goods on the shelves.
All this allows the staff to devote more time to customers with the most complex and potentially more profitable requests. At the same time, the efficiency of each store is improved by constantly maintaining an inventory.
Autonomous transportation
Billions of factors influence autonomous vehicles' development, public acceptance, and spread. Let's find out how AI affects the progress of autonomous vehicles.
For autonomous vehicles to be widely accepted by mankind, they need to become nothing less than smarter than live drivers. This is only possible if developers succeed in making mechanisms capable of understanding, learning, and predicting environmental changes. Without AI, this is absolutely impossible.
Autonomy today is based on the sensor technology stack shown in the image above. The main task of such a stack of technologies is to produce a 3D map surrounding the car in the highest possible resolution. Deep learning algorithms embedded in the car's onboard system process a live stream of information about the environment to identify obstacles and other cars, interpret road signs and markings, traffic light signs, and comply with speed limits and traffic rules in time.
It is obvious that rigidly programming the car "brain" for all possible driving scenarios is impossible in principle. Consequently, there is nothing left for developers but to use constant knowledge acquisition through deep learning mechanisms. It is necessary to create "constantly learning" cars.
Today, the traditional players in the automotive market (BMW, Daimler, Ford, Toyota, and VW) are already using AI as a critical component of their development of autonomous vehicles. But we first heard about this concept from initially little-known automotive players such as Google, Tesla, and Waymo. These companies are developing their automotive cars using proprietary artificial intelligence mechanisms.
On the other hand, automotive industry suppliers such as Bosch, Mobileye, Nvidia, Quanergy, and ZF are making sensors, algorithms, and datasets to accelerate and support the further development of autonomous vehicles. And on top of that, in partnership with established automotive giants, mobile platforms such as Lyft and Uber are moving toward systems offering on-demand autonomous cab rides.
Convenience, lower production costs, increased efficiency, lower emissions, and fewer accidents are all primary drivers for autonomous vehicles. Thanks to falling prices of electronic components, increasing efficiency of deep learning algorithms, and a growing knowledge base on autonomous driving, all these technologies are advancing faster and faster every day. Nevertheless, the full implementation of a "car without a live driver" will require significant regulatory changes in each country where such vehicles are expected to operate. It will take some time.
Engineering and manufacturing: artificial intelligence is shaping the physical world
The use of AI in engineering and manufacturing marks the beginning of our transition to a completely digital world. What is meant by this? Today, the digital world can change the physical world - the one that surrounds us. For example, industrial conglomerate General Electric is strategically focused on bringing non-stop energy and transportation to the world's population. In doing so, they are aided by the use of AI in the non-stop production of heavy machinery.
In the agricultural sector, John Deere is grappling with a global challenge: how to feed a ten billion-person population with very limited land suitable for agriculture.
The company uses IBM Watson and a smart manufacturing platform in assembly and repair processes at its largest plant in Mannheim, Germany. Workers on the shop floor use cell phones as a workstation connected to the Watson platform. It uses an AI algorithm that recognizes video images and can identify faults online. The worker promptly receives information from the system on how to fix the fault. The employee does not need to be distracted by the phone for long - Watson works through voice commands recognized by the machine's natural language processing.
The production system is also connected to the smart order processing system. So once a problem is identified, the necessary spare parts are identified, ordered, and automatically delivered to the breakdown location. Ultimately, if sub-specialists are required for the repair, the smart manufacturing platform will check the time slots of these specialists and suggest the most optimal time for the repair.
Conclusion
As humanity embrace the myriad possibilities of AI, it is essential to tread carefully and address the ethical, privacy, and regulatory challenges accompanying this technology. Responsible development and thoughtful implementation will ensure that AI continues elevating our lives, making the world a more connected, efficient, and innovative place for future generations.
In conclusion, the journey of AI is a testament to human ingenuity and the boundless potential of technology. The present landscape offers a glimpse of the immense possibilities yet to unfold, and with responsible stewardship, AI will undoubtedly shape a future that surpasses our wildest imaginations.
As we stand at the crossroads of technological advancements, the MaybeWorks team eagerly awaits the unfolding chapters of this remarkable story - a story in which AI is not just everywhere but serves as a catalyst for a brighter and more harmonious world. Feel free to rely on us to augment your development teams with high-tech JavaScript/ TypeScript experts.
