In the big world of IoT, location tracking becomes the next frontier! Location tracking for humans is already becoming an integral part of our lives, especially for navigation. Traditional technologies are not only expensive but they have their technical boundaries which prevent successful scaling. For the IoT geolocation to become a reality, you must be accurate extremely and significantly low cost.
Geolocation services have been around for a long time and used to provide the real-world location of objects like speed radars, mobile phones, and internet-connected computers. In old times, services were widely based on supporting GPS (Geographic Positioning System) which lacks indoor coverage and power efficiency which has given rise to many location technologies that use RFID, UWB (Ultra-Wide Band), Bluetooth and LoRa to provide indoor GPS-free geolocation.
IoT and Geolocation
The rise of IoT is increasing dramatically with the growing demands for geolocation services. It is proliferating at an exponential rate by the increasing billions of IoT-connected devices.
The complex applications can obtain accurate info about the location of people and their devices in this landscape to adapt the app logic into dynamic environments. A geolocation service is a virtual sensor that provides the position of physical objects from an IoT viewpoint as the operation of this virtual sensor is based on data derived by multiple physical sensors such as radio receivers, barometers, and accelerometers that are coupled with signals and data processing algorithms.
How Geolocation Services would be a core element of Future IoT platforms?
In the variant sectors of the economy, all the operations need to deploy general-purpose IoT platforms to build so-called smart factories, smart airports, smart utilities, smart hospitals and much more on the basis of few critical needs:
All the generic IoT infrastructures avoid the need of deploying an ad-hoc infrastructure for a new IoT use case that is based on the emerging Low Power Wide Area Networks (LP WAN) technologies which take care of object communications, management, storage, security, IT integration and decreasing the cost of IoT deployments.
Emerging data analytics tools like Big Data and Deep Learning technologies allows the raw data collected by IoT infrastructures into high-level knowledge and business intelligence tools. The value these tools bring to the IoT is pervasive into many organizations.
Integrating the geolocation as a core service of IoT infrastructure provides multiple benefits which include:
Removes the need for a separate implementation of geolocation functionalities for each of the various use cases by reducing development costs and increasing the potential economy scale.
Ease out the integration and interoperability of the location sensors with other elements of the IoT platform such as maintenance of gas bottle on a large industrial or healthcare site by combining a maintenance sensor with a position sensor and workflow specific logic.
IoT’s location: Spreading Awareness
Integration of geolocation within IoT platforms opens up new horizons in the development of IoT-based geolocation use cases. The innovation in geolocation is gradually moving from the deployment of location-aware devices to the provision of integrated location services which exploit analytics and data mining to drive location awareness among business processes.
Enterprises will be offering new ways to leverage location information in their workflow processes as the innovators have a rising opportunity to develop the next gen location-aware services by offering several benefits before their competitors. One should also look at some negative side or challenges when integrating geolocation services within IoT platforms.
Provides support to multiple use cases based on a single location infrastructure:
There ain’t any ‘one size for all’ location technology or method that will fit all use cases. Hence, the applicability of each geolocation methods will depend on multi-dimensional requirements of the use cases like autonomy, precision, object number, scope, and size a physical environment. Few emerging geolocations will create awareness among IoT infrastructures by supporting these multiple methods meet such varying use case requirements.
Low-power and Scalable indoor operations:
Currently, the IoT infrastructures are getting deployed in large-scale sites by interconnecting these objects they are offering to run on battery power. Geolocation aware infrastructures are coping with scalability and low power requirements at the same time.
These challenges will help to boost the market penetration of IoT-based geolocation services as the future of IoT is location-aware without any shadow of doubt and you should be prepared for its arrival. Keep Learning!
The Significant Potential of Artificial Intelligence in Blockchain
Artificial Intelligence and blockchain are two buzzwords that seem to stimulate great interest in the tech community today. While both technologies are said to be cutting edge and futuristic, there seems to be little in common between the two. As such, the idea that the two can be incorporated, much less to the mutual benefit of each other, seems far-fetched. But this is far more feasible than you thought.
Blockchain and AI: Far yet close
Artificial intelligence was first thought of in the 1950s, but it was only since the 90s that any real progress was made. Today, AI has filtered through the market chain and is now available as a consumer product in the form of virtual assistants like Siri and Alexa. Despite the great interest in the field, AI hasn’t grown as fast as other technologies over the decades.
As you might have read through Bitcoin News, Blockchain technology is a decentralized digital ledger spread across systems (called nodes) for storing data. These chunks of data, once formed into blocks, are immutable and cannot be changed. They are also encrypted to ensure security.
While both AI and blockchain seem like two very different technologies, there is one thing that binds them: data. AI works by hogging big data for training, while blockchain is used to store huge amounts of data.
Incorporating AI into Blockchain
Blockchain technology, being decentralized, operates by creating nodes (essentially separate systems) on a network where each node stores a portion of data; all of this data is free of any central control. However, if every node has access to data, it can put the privacy and security of the entire data at severe risk. To deal with this, blockchain encrypts the data before storing it in the nodes.
This works great for safety but puts a great hurdle on accessibility. After all, how does one access such data? Encryption is necessary and giving the decryption key to anyone will defeat the whole purpose of decentralization.
Artificial intelligence works as the perfect solution to this dilemma. Since AI is a non-person, having it access the encrypted data does not put privacy at risk. AI can also provide a great interface that decrypts the required data in the background and providing users with the results they asked for.
Nonetheless, there is more than one arena where AI benefits blockchain. The entire blockchain network works on huge and complex instruction sets that decide what happens where across the nodes. Currently, the procedure of creating and executing these instruction sets is tedious and not very efficient. However, AI can be trained to execute these instruction sets with optimum efficiency, thus improving the functioning of blockchain to a great degree.
Incorporating Blockchain into Artificial Intelligence
Any AI model goes through an extensive training process where a huge amount of data is fed, and the model is guided to give the right answers. Through innumerable passes, the AI learns to give answers that might even surpass expectations. But how exactly does it manage to do that? We don’t know. The AI acts like a black box: we know the input and the output but can’t be sure of how the transition took place.
Blockchain technology can vastly help in that regard. Since blockchain is decentralized and spread across entire networks, there is no real limit to how much data it can store. All of this data is also immutable. When you incorporate blockchain into AI, it can store the information of each training pass, including details like weights assigned. This treasure of data can actually become a goldmine in demystifying artificial intelligence and making vast improvements in how we make intelligent machines.
Bottom Line
Blockchain and AI might not look like the match made in heaven, but they have a huge potential of mutual benefitting if used correctly. No doubt, they can do wonders in the development of many sectors when combined together.
Beauty industry embraces big data, AI to win over Gen Z
An increasing number of skincare and cosmetics companies here are utilizing big data and artificial intelligence (AI) technologies for personalized marketing strategies aimed at attracting the post-millennial generation, known as Gen Z, according to industry officials. The beauty industry is focusing on tailored services and customization of products, such as a serum meant for a specific type of skin. Innisfree has teamed up with a research team led by Kim Dae-shik, a professor at the Department of Electrical Engineering at KAIST, to analyze its consumer database and offer personalized services based on 1 million online product reviews compiled from January to December 2018. Kim and his research team developed a machine learning algorithm to categorize good or bad products based on customers' feedback and skin types. The team also constructed positive and negative lists of ingredients for each product. Innisfree said it will offer customized skincare advice, such as recommending products frequently used by customers or introducing new products to better reflect customer needs. The company expects the use of big data will eventually help it understand user habits and guide it in customized production and self-service. "We have been pursuing digital innovation by analyzing the needs of millennials and Gen Z, who are familiar with digital devices, pop-up stores and cosmetics vending machines," an Innisfree official said. "Our business innovation through digitization will give us a way to survive in the competitive retail industry." Meanwhile, Lotte Department Store has been utilizing pop-up retail store On and The Beauty as part of a larger strategy to attract younger consumers. With self-service screens, consumers can get help from a beauty stylist on product information and try out new products by simply pressing a button. A Search ON button guides a customer to sales rankings and item locations, while a Touch ON button allows them to find out about product ingredients. A Catch ON button calls for a salesperson to recommend customized products and offer make-up services. "We provide a big data-based beauty curation service to enhance the customer experience," a Lotte Department Store official said. Another cosmetics brand IOPE has utilized big data in product development. The company gathered data of customers' skin conditions checked at an IOPE counseling lab to develop new ampoule product "Stem 3." "Retailers have been providing product recommendations based on customers' established habits or preferences," said Shin Byung-joo, a professor at the Department of Computer Science at Sejong University. "In the future, they will use big data or AI to develop new products and offer alternative items." source: http://www.koreatimes.co.kr/www/tech/2019/05/694_268601.html
8 factors shaping the future of big data, machine learning and AI
AI and machine learning combined with ever-increasing amounts of data are changing our commercial and social landscapes. A number of themes and issues are emerging within these sectors that CIOs need to be aware of.
I’ve just spent a couple of days at O’Reilly’s Strata Data Conference in London and got a much better idea where the world of big data, machine learning (ML) and AI may be heading. These sectors have developed rapidly over the last 5 years with new technologies, processes and applications changing the way organisations are managing their data.
The Strata conference provides a good barometer of what the state-of-the-art is in big data manipulation as well as the concerns of developers and users. Eight key points emerged for me from the event.
1. 5G will stimulate the growth of ML and result in new applications and services
I spoke with O’Reilly’s Chief Data Scientist and Strata organiser, Ben Lorica about this and he sees the increased bandwidth and flexibility of 5G as well as the move to edge computing as key enablers. He pointed out that China is a leading global force in this technology but that many firms are still working out the business models for all the 5G investments they are making.
2. Changing skillsets for data scientists
Cassie Kozyrkov, Google Cloud’s chief decision scientist, pointed out in her talk that as the UX for ML tools is improved, the skills required will become less technical and more focused on the ability of data scientists to work across silos and be more integrated into the business.
3. The online and offline worlds are merging
China’s Alibaba ecommerce group and Amazon are experimenting with physical store spaces while bricks and mortar stores are still adapting to the new online world. It feels to me that the offline moves by ecommerce groups are offensive while the online investments by physical retailers are defensive. There is still a long way to go before this fully plays out but the expertise that companies like Amazon and Alibaba have with managing data at scale gives them a key advantage.
4. Internal data platforms are becoming essential for growth and innovation
Presentations from data scientists at Lyft and BMW showed how putting data platforms at the centre of new product development and business process management are driving innovation. While this may come naturally for digitally native companies like Lyft it is also something that traditional, industrial companies are having to engage with as data generating sensors become embedded within products.
5. Open data needs to be taken as seriously as open source software
We all know that open source software is behind the rise of many big data and ML products and services. The commercial and technical case for open source was proven years ago. However, much less attention has been paid to the importance of open data for innovation. The outputs of algorithms are only as good as the quality of the data that goes into them.
Chris Taggart, co-founder and CEO OpenCorporates, the biggest open database of companies in the world, highlighted the problems that companies run into when they rely on proprietary datasets where data provenance may be sketchy and meta data not shared across products. Open data is more transparent and does not lock firms into expensive commercial contracts that can be very difficult for companies to wean themselves off.
6. Importance of capturing and managing real-time data
While real-time or near real-time data is not always required for AI and ML projects, the ability to build systems that can handle it can be a valuable form of competitive advantage. As data-driven decision making become more embedded within organisations the competitive edge will sometimes go to those that can respond more quickly to events. The scale and breadth of offerings from Amazon Web Services in this respect show how the tools to do this are becoming easier and cheaper to access.
7. Legal and ethical issues are starting to change how firms innovate
A talk by Dr Sandra Wachter of Oxford University highlighted an issue that, I suspect, will become more discussed over the coming year or two. She pointed out that many firms are now aware of their obligations to protect personal data as initiatives such as the GDPR have come into force. However, a less discussed issue and one that regulators are still grappling with is that of inference and the decisions that are being made by embedded algorithms based on the data they are processing.
We have a right, in Europe at least, to see what data is being held on us and, to varying degrees, have it corrected or removed. However, we do not have the same redress with the assumptions that firms may be automatically making about us because of this data in areas such as credit checking and health insurance.
8. “To those that have shall be given”
As the conference came to a close, I started thinking about how smaller companies without access to the massive datasets of the internet giants or global FMCG firms will be able to compete in the age of big data and algorithmic-decision making. There is the danger, and perhaps we are already seeing it, of virtuous circles of innovation utilising network effects from online services cementing the position of large companies.
However, as Shivnath Babu, co-founder and CTO of Unravel Data Systems, pointed out to me, the internet and app economy is still capable of allowing small firms to leverage data from their apps and online activities and make an impact on markets. Perhaps this and the rise of open data emanating from public data sources will provide the basis for a new generation of start-ups to change the world in the way that Google, Facebook and Amazon have over the last 20 years.
Choosing the right data security solution for big data environments
Data is money. For some organisations, data has become the highest commodity, and this means consumers now hold the power. By gathering these large data sets, businesses can analyse human behaviour and interactions through trends, market patterns and associations which will fundamentally lead them to make business decisions to tailor experiences for consumers. Big data is big business and enterprises of all sizes are investing in data science and analytical platforms. Whenever data is mentioned, security should automatically follow; especially when you consider big data is everywhere – on-premise, in the cloud, streaming from sensors and devices, and moving further across the internet.
Yet, the security aspect of protecting these data sets is often overlooked. In the last five years, the surge of data breaches has seemingly run parallel with the amount of data organisations are now demanding. Yahoo, Facebook, Dropbox, Equifax, Twitter and Google are just some of the high-profile companies that are well known to collect big data, but also share the unwanted tag of experiencing a data breach. With much of the collected data by companies classified as sensitive personal information, cybercriminals are determined now more than ever to get their hands on it for malicious use.
Carrying the baton for protection of personal information in this area is data-centric security. Data-centric security attempts to focus on the data itself – altering or disguising it as a means of protection from misuse and prying eyes - rather than traditional routes which may focus on the IT infrastructure or security of systems. Yet, choosing the right solution from an increasing array of data-centric options can prove tricky. Vendors are quick to state they provide a solution that is data-centric, but often such solutions fail to also meet the stringent demands of being able to use it in a big data analytics environment.
The ideal data-centric solution requires a number of crucial aspects to meet the needs of tomorrow’s analytical workloads. To protect big data effectively and adequately, solutions need to incorporate the following
Scalability
Naturally, big data environments will be in constant use and so housing security that can keep pace is a must.
Therefore, the data-centric solution needs to have the ability to scale any workload regardless of whether it’s in real-time or for historical use cases and without any visible impact or hinderance to performance.
Uninterrupted performance
With the involvement of artificial intelligence and machine learning in many of today’s software programs, businesses are looking to take advantage and incorporate this technology within their systems. The reason: the ability to process more data with less man-power.
With ‘speed is key’ as the motto, security needs to keep up with the pace of the business. The only way to achieve this is by seeking a data-centric security solution that delivers on its intelligence features for both streaming and load distribution.
Availability and adaptability
Vital qualities such as availability and adaptability are required, and security should never hinder these elements. If for any reason issues arise, the security technology needs to function with built-in fault tolerance capabilities to guarantee these are resolved automatically and without interruption to overall system functionality.
To achieve maximum value and protection, the data-centric solution will need total access to wherever the data may lie.
Flexibility
Many enterprises employ big data frameworks such as Spark, MapReduce and Hadoop to provide interconnected platforms, systems and standards to carry out big data projects.
However, these open source environments are operating on legacy systems, and with new technologies continually sprouting up, the data-centric solution needs to offer a degree of flexibility to ensure it does not become outdated.
All environments covered
With the rise of digital transformation, cloud environments have become very attractive for all businesses. Part of the security checklist requires the solution to support an organisation’s current and future big data analytics environment, whether that’s on-premise, in the cloud or both (hybrid) – or even in a multi-cloud setup.
When it comes to implementing a security solution with big data analytics tools and platforms, to minimise needlessly wasting resources and any time taken to change already installed applications, the platform should be accommodating in terms of being able to handle both native and API-based integration.
Tokenisation
Ultimately, the best way to protect sensitive data and enable analytics is through tokenisation or substituting a sensitive data element with a non-sensitive equivalent (known as a token). By tokenising critical data, the analysts are able to extract insights without the risk of exposing personal, confidential data. This eliminates one of the prime issues with classic security solutions which can’t protect sensitive data wherever it goes. Organizations need to implement security and privacy protection that travels with the data itself across hosting models, locations, and devices. Tokenisation is a key capability when it comes to enabling a zero trust architecture across the Enterprise.
Data-centric security solutions that meet these criteria will better serve companies for years to come as the amount of data collected grows and privacy and data protection concerns become mainstream and litigious.
Artificial intelligence and its impact on accountancy Accountants' job are on the verge of becoming much more exciting thanks to artificial intelligence
AN accountant's everyday life can often seem somewhat repetitive or uneventful, unless of course it's forensic accounting or connected in some way to a multi-billion business merger.
But the accountant's job might just be on the verge of becoming much more exciting! When we mention the term artificial intelligence (AI), we are inclined to think primarily of robots. We think of how much more of the mundane jobs that robots can take care of for us. As a result, many jobs and careers change and become more advanced and exciting.
That excitement is about to be a part of every accountant's daily life. AI is making more and more of an impact on the accounting profession and more Irish companies need to consider getting on board with it.
Accounting is traditionally a process and systems-based industry, where micro decisions are made every day. Artificial intelligence allows scalable decisions to be made for repetitive high volume tasks, allowing accountants to focus on providing quality advisory services which add much more value to a business. This allows the company to be more efficient and improve its quality of service to its clients.
Companies want much more from their accountancy departments than they did ten years ago. They expect their accountants or financial controllers to add value to their business as well as making their life simpler. As a result, pro-active accountants are looking at introducing as much technology as possible, to enhance their contribution to the company goals.
It's thought that AI could be the biggest invention since the calculator, by going beyond a calculation, and actually assisting with processing the data. Accountancy will change drastically with the onslaught of this technology, because it has such a high volume of repetitive processes. The quality of what the accounts department can offer will improve greatly, because human error will lessen, and even go completely in some areas.
Speaking with global leaders like technology giant SAP, I believe we will also see more specialist uses of AI in the accounting industry as technology firms and accountants collaborate to find innovative solutions for accountants.
Gone are the days of pen and paper schedules. Contrary to this, highly switched on, tech savvy accountants will become even more relevant. Whilst AI will change the way work is done, this does not necessarily mean accountants will be out of touch. The position of the company accountant, will become even more important within the business.
Accountants will further focus on how they can add value to the company, rather than allowing themselves to be viewed as just a number cruncher, or someone who reports on the financial position of the company, pays the bills or oversees the end of month figures, for the monthly board meeting.
The accountant, as a result of AI, will take on board a much more advisory role in the future. The new breed of accountants, coming from a younger generation and growing up with this new technology, will expects a high degree of automation in the accounts office.
AI is currently in its early stages in the accountancy sector, however it is going to radically change the accountants function in industry.
The ability of accountants to focus on using their skills to drive business benefits, will be the key to the use of AI in the accountancy sector. With the new applications, I expect ever increasingly complex decisions being made by AI, from posting invoices, to the correct account automatically, to alerting the accountant, when a client is showing signs of cash shortage.
By using various integrations, AI will be able to pull data from various sources and process this into high quality management information.
What Is Artificial Intelligence (AI)? The TL;DR is that AI is the science of building computers that can solve problems the way humans do. But there's much (much) more to it than that.
In September 1955, John McCarthy, a young assistant professor of mathematics at Dartmouth College, boldly proposed that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."
McCarthy called this new field of study "artificial intelligence," and suggested that a two-month effort by a group of 10 scientists could make significant advances in developing machines that could "use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves."
At the time, scientists optimistically believed we would soon have thinking machines doing any work a human could do. Now, more than six decades later, advances in computer science and robotics have helped us automate many of the tasks that previously required the physical and cognitive labor of humans.
But true artificial intelligence, as McCarthy conceived it, continues to elude us.
What Exactly Is AI?
A great challenge with artificial intelligence is that it's a broad term, and there's no clear agreement on its definition.
As mentioned, McCarthy proposed AI would solve problems the way humans do: "The ultimate effort is to make computer programs that can solve problems and achieve goals in the world as well as humans," McCarthy said.
Andrew Moore, Dean of Computer Science at Carnegie Mellon University, provided a more modern definition of the term in a 2017 interview with Forbes: "Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence."
But our understanding of "human intelligence" and our expectations of technology are constantly evolving. Zachary Lipton, the editor of Approximately Correct, describes the term AI as "aspirational, a moving target based on those capabilities that humans possess but which machines do not." In other words, the things we ask of AI change over time.
For instance, In the 1950s, scientists viewed chess and checkers as great challenges for artificial intelligence. But today, very few would consider chess-playing machines to be AI. Computers are already tackling much more complicated problems, including detecting cancer, driving cars, and processing voice commands.
Narrow AI vs. General AI
The first generation of AI scientists and visionaries believed we would eventually be able to create human-level intelligence.
But several decades of AI research have shown that replicating the complex problem-solving and abstract thinking of the human brain is supremely difficult. For one thing, we humans are very good at generalizing knowledge and applying concepts we learn in one field to another. We can also make relatively reliable decisions based on intuition and with little information. Over the years, human-level AI has become known as artificial general intelligence (AGI) or strong AI.
The initial hype and excitement surrounding AI drew interest and funding from government agencies and large companies. But it soon became evident that contrary to early perceptions, human-level intelligence was not right around the corner, and scientists were hard-pressed to reproduce the most basic functionalities of the human mind. In the 1970s, unfulfilled promises and expectations eventually led to the "AI winter," a long period during which public interest and funding in AI dampened.
It took many years of innovation and a revolution in deep-learning technology to revive interest in AI. But even now, despite enormous advances in artificial intelligence, none of the current approaches to AI can solve problems in the same way the human mind does, and most experts believe AGI is at least decades away.
The flipside, narrow or weak AIdoesn't aim to reproduce the functionality of the human brain, and instead focuses on optimizing a single task. Narrow AI has already found many real-world applications, such as recognizing faces, transforming audio to text, recommending videos on YouTube, and displaying personalized content in the Facebook News Feed.
Many scientists believe that we will eventually create AGI, but some have a dystopian vision of the age of thinking machines. In 2014, renowned English physicist Stephen Hawking described AI as an existential threat to mankind, warning that "full artificial intelligence could spell the end of the human race."
In 2015, Y Combinator President Sam Altman and Tesla CEO Elon Musk, two other believers in AGI, co-founded OpenAI, a nonprofit research lab that aims to create artificial general intelligence in a manner that benefits all of humankind. (Musk has since departed.)
Others believe that artificial general intelligence is a pointless goal. "We don't need to duplicate humans. That's why I focus on having tools to help us rather than duplicate what we already know how to do. We want humans and machines to partner and do something that they cannot do on their own," says Peter Norvig, Director of Research at Google.
Scientists such as Norvig believe that narrow AI can help automate repetitive and laborious tasks and help humans become more productive. For instance, doctors can use AI algorithms to examine X-ray scans at high speeds, allowing them to see more patients. Another example of narrow AI is fighting cyberthreats: Security analysts can use AI to find signals of data breaches in the gigabytes of data being transferred through their companies' networks.
Rule-Based AI vs. Machine Learning
Early AI-creation efforts were focused on transforming human knowledge and intelligence into static rules. Programmers had to meticulously write code (if-then statements) for every rule that defined the behavior of the AI. The advantage of rule-based AI, which later became known as "good old-fashioned artificial intelligence" (GOFAI), is that humans have full control over the design and behavior of the system they develop.
Rule-based AI is still very popular in fields where the rules are clearcut. One example is video games, in which developers want AI to deliver a predictable user experience.
The problem with GOFAI is that contrary to McCarthy's initial premise, we can't precisely describe every aspect of learning and behavior in ways that can be transformed into computer rules. For instance, defining logical rules for recognizing voices and images—a complex feat that humans accomplish instinctively—is one area where classic AI has historically struggled.
An alternative approach to creating artificial intelligence is machine learning.Instead of developing rules for AI manually, machine-learning engineers "train" their models by providing them with a massive amount of samples. The machine-learning algorithm analyzes and finds patterns in the training data, then develops its own behavior. For instance, a machine-learning model can train on large volumes of historical sales data for a company and then make sales forecasts.
Deep learning, a subset of machine learning, has become very popular in the past few years. It's especially good at processing unstructured data such as images, video, audio, and text documents. For instance, you can create a deep-learning image classifier and train it on millions of available labeled photos, such as the ImageNet dataset. The trained AI model will be able to recognize objects in images with accuracy that often surpasses humans. Advances in deep learning have pushed AI into many complicated and critical domains, such as medicine, self-driving cars, and education.
One of the challenges with deep-learning models is that they develop their own behavior based on training data, which makes them complex and opaque. Often, even deep-learning experts have a hard time explaining the decisions and inner workings of the AI models they create.
What Are Examples of Artificial Intelligence?
Here are some of the ways AI is bringing tremendous changes to different domains.
Self-driving cars: Advances in artificial intelligence have brought us very close to making the decades-long dream of autonomous driving a reality. AI algorithms are one of the main components that enable self-driving cars to make sense of their surroundings, taking in feeds from cameras installed around the vehicle and detecting objects such as roads, traffic signs, other cars, and people.
Digital assistants and smart speakers: Siri, Alexa, Cortana, and Google Assistant use artificial intelligence to transform spoken words to text and map the text to specific commands. AI helps digital assistants make sense of different nuances in spoken language and synthesize human-like voices.
Translation: For many decades, translating text between different languages was a pain point for computers. But deep learning has helped create a revolution in services such as Google Translate. To be clear, AI still has a long way to go before it masters human language, but so far, advances are spectacular.
Facial recognition: Facial recognition is one of the most popular applications of artificial intelligence. It has many uses, including unlocking your phone, paying with your face, and detecting intruders in your home. But the increasing availability of facial-recognition technology has also given rise to concerns regarding privacy, security, and civil liberties.
Medicine: From detecting skin cancer and analyzing X-rays and MRI scans to providing personalized health tips and managing entire healthcare systems, artificial intelligence is becoming a key enabler in healthcare and medicine. AI won't replace your doctor, but it could help to bring about better health services, especially in underprivileged areas, where AI-powered health assistants can take some of the load off the shoulders of the few general practitioners who have to serve large populations.
The Future of AI
In our quest to crack the code of AI and create thinking machines, we've learned a lot about the meaning of intelligence and reasoning. And thanks to advances in AI, we are accomplishing tasks alongside our computers that were once considered the exclusive domain of the human brain.
Some of the emerging fields where AI is making inroads include music and arts, where AI algorithms are manifesting their own unique kind of creativity. There's also hope AI will help fight climate change, care for the elderly, and eventually create a utopian future where humans don't need to work at all.
