Terms Beginning With 'a'
Artificial Intelligence (AI)
- September 17, 2020
- Team Kalkine
What is Artificial Intelligence?
A popular and widely used technology, Artificial Intelligence (AI), in simple terms, provides abilities to machines to perform a particular task that helps in reducing human effort. This is made possible via specific programming languages, tools & techniques, or codes, that are implemented into the machine so that it can perform tasks with minimal or no human intervention.
History of AI and how it came into existence?
The scope of Artificial Intelligence is massive, and it continues to grow each day. Though Artificial Intelligence has gained prominence lately, it may sound fascinating to know that scientists started to think about this technology in the early parts of the twentieth century.
Alan Turing is considered amongst the most extraordinary scientist of the 20th century who created history by laying a strong theoretical foundation of computer science. He was a mathematician, cryptanalyst, logician, and a philosopher.
In 1936, Alan Turing, in an article, introduced a theoretical device that was based on the notion that a machine can imitate any other machine (known as the Turing machine).
AI Timeline:
What exactly do we mean by AI?
As highlighted above, Artificial Intelligence was coined in the year 1956 by John McCathy. He defined AI as science and engineering to make intelligent machines. It refers to the development of a computer system that has the potential to perform a task that usually needs human intelligence like speech identification, decision making, visual perception and interpretation between languages.
We live in an era where we see the role of Artificial Intelligence in the form of robots, a machine which has vast application in recent times. AI application can be seen in healthcare, sales and marketing, business analytics and so on.
INTERESTING READ: Will Artificial Intelligence Barge Higher in Post-Pandemic Era?
Some Real-world Applications of AI:
- Google’s Predictive Search Engine: One of the most common examples of AI is the Google predictive search engine. We have observed that when we start typing anything on Google, a set of recommendation start showing from where we can choose. This is one of the simplest examples of Artificial Intelligence in action.
- Legal Document Analysis: A well-recognized Investment Banking company uses its contract Intelligence platform to access its legal document. Through the use of this AI platform, the time to access the legal document was reduced drastically. Although AI does not have the reasoning power, however, with the advancement in the technology now, Artificial Intelligence can solve the most complex tasks easily.
- Social Media/Networking Platforms: Popular website Facebook is where we can see the implementation of AI. Facebook uses machine learning and deep learning to identify the facial features of a person, and based on that, people can tag their friends. The auto-tagging feature which we see on Facebook is an example of AI.
- Voice-activated Personal Assistants such as Google Alexa and Apple are popular examples of AI.
- Self-driven cars: Artificial Intelligence plays a crucial role in self-driven vehicles with the best example being that of Tesla. Through the tech, the cars are capable of detecting any obstacle coming on its way.
- Email Applications: In Gmail, Artificial Intelligence helps to sort those mails which are spam based on specific keywords by using machine learning and natural language processing.
- Online Streaming Services: Netflix is yet another example of AI implementation, where movie recommendations are provided based on the type of search and studying the pattern of movies preferred by the user.
What are the Different Types of Artificial Intelligence?
There are three types of Artificial Intelligence:
Artificial Narrow Intelligence:
Artificial Narrow Intelligence is also known as weak AI, where artificial intelligence is applied to a specific task. A typical example of an Artificial Narrow Intelligence would be Alexa, which operates within a specified range. All the AI applications we see at present are Artificial Narrow Intelligence.
Artificial General Intelligence:
Artificial General Intelligence is known as strong AI, and it involves a machine that is capable of performing any intellectual task just like a human being. Till now, we have not reached this stage as there is no machine in the present times that have reasoning ability like human beings.
Artificial Super Intelligence
Artificial Super Intelligence refers to that point of time when the capability of the computer surpasses the capacity of a human being.
Advantages and disadvantages of Artificial Intelligence
After having a fair understanding of AI, let us look into the advantages and disadvantages of artificial intelligence.
Advantages of using Artificial Intelligence
Zero human error
With the usage of Artificial intelligence, we would be able to nullify human error completely. The accuracy of the final results would increase. The only thing required here is that the machine is programmed correctly. Once this is done, the devices are able to make accurate decisions according to the previous information that they collect over time based on their past experience based on specific algorithm sets. Thus, it reduces the chances of error and improves efficiency.
Zero risks
Through AI, the task which could be dangerous to humans can be eliminated, and the results obtained through machines would yield good results. For example, through a machine it would be possible to identify any earthquake quickly and faster action could be taken.
24 hours availability
Human beings have their own working capacity. However, when machines are considered, they have the potential to work round the clock. These AI machines do not get bored or exhausted, even though they have to repeat the same task multiple times.
Nowadays, we see a lot of customer care queries been taken care of via AI technology. The most common example here is chatbots.
Make faster decisions
AI and other technologies can help make better data-driven decisions at a much faster rate than human beings.
Moreover, the machine lacks emotions, hence the output we would derive would be unbiased. On the other hand, human beings may get emotionally carried away, but this is not the case with AI machines. They are extremely efficient in result-oriented decision making.
Disadvantages of Artificial Intelligence
AI machines are expensive
Considering the complexity of an AI-enabled machine, AI technology can be pretty expensive. Designing a machine that can mimic a human being involves a huge amount of time, patience, money, and energy, making this technology expensive.
Lack of creativity
AI machines lack creativity because whatever actions these machines do are pre-programmed. AI machines are capable of doing a particular task. The biggest disadvantage here is that this technology cannot think out of the box. This is because the AI machine would analyse the situation and based on the pre-fed data along with the data gathered via past experience, the machine gives the output.
Possibilities of job losses
As AI technology reduces human effort and improves accuracy, most of the repetitive tasks have now been automated. With these features, businesses are gradually looking to switch to these technologies. Some of these jobs include the data entry operator, talking to the customer, chat support etc.
Future of AI - Glance at Robot Sophia and Robot Rashmi
AI has come a long way since its inception, and there is much more to come in the future. Let us look at AI-driven robots that have gained significant attention in the last couple of years.
Robot Sophia
Sophia is a humanoid robot that was built by Hanson Robotics. Sophia is a blend of science, engineering, as well as creativity. She is a human-crafted science fiction personality that can be considered the hope of AI technology & robotics.
Sophia is capable of catching the vision of global viewers. She is also the first robot citizen in the world and the first robot Innovation Ambassador for the UN development program.
Source: © Toxawww | Megapixl.com
Sophia’s underlying AI parts can be fused in different ways. Hence, her response could be unique as per any given circumstances or communication. Sophia can use machine perception to distinguish human faces, identify hand movements, and see emotional expressions. She is capable of assessing feelings during any conversation and can support finding a solution to achieve the target. She also its own emotion and can nearly mimic human evolutionary psychology & several areas of the brain. Sophia has IK solvers & path planning that controls the hands, stare, and locomotion plan. Her walking body can do dynamic balance for adaptive walking over several terrains.
Robot Rashmi
Robot Rashmi is an Indian version of Robot Sophia that can speak multiple languages like Hindi, Marathi, and Bhojpuri. She is the RJ at one of the popular radio stations in India.
Robot Rashmi was developed by a 38-year-old Indian software engineer. Robot Rashmi is India’s first lip-syncing robot that uses artificial intelligence, linguistic interpretation, visual data, and face interpretation that supports Robot Rashmi to act and respond to any question asked.
Some Other Coolest humanoid robots:
- Ocean One
- Atlas
- Nao
- Petman
- Robear
- Pepper
- Mitra
- Junko Chihira
- Erica
- Geminoid DK
- Nadine
- Jia
Artificial Intelligence Outlook 2030
Artificial Intelligence has a broad scope that is beyond one’s imagination. AI has the potential to transform productivity and GDP potential of the global economy. To make this possible, there is a need for significant investment in various AI technologies.
As per a research report released by PWC in 2017, 45% of the total economic gains by 2030 would be through product improvements. It would be possible with the help of Artificial Intelligence which would support in increased customization, improved attractiveness, and affordability.
The report also highlighted that AI would contribute over US$15.7 trillion to the global economy. It would boost the economy by 26% in GDP.
INTERESTING READ: Tech hot Space: AI and Pitch Towards a Big Industry Opportunity
Related Definitions
Data Mining
What is Data Mining? Data mining is a process that facilitates the extraction of relevant information from a vast dataset. The process helps to discover a new, accurate and useful pattern in the data to derive helpful pattern in data and relevant information from the dataset for organization or individual who requires it. Key Features of data mining include: Based on the trend and behaviour analysis, data mining helps to predict pattern automatically. Predicts the possible outcome. Helps to create decision-oriented information. Focuses on large datasets and databases for analysis. Clustering based on findings and a visually documented group of facts that were earlier hidden. How does data mining work? The first step of the data mining process includes the collection of data and loading it into the data warehouse. In the next step, the data is stored and managed on cloud or in-house servers. Business analyst, data miners, IT professionals or the management team then extracts these data from the sources and accordingly access and determine the way they want to organize the data. The application software performs data sorting based on user’s result. In the last step, the user presents the data in the presentable format, which could be in the form of a graph or table. Image Source: © Kalkine Group 2020 What is the process of data mining? Multiple processes are involved in the implementation of data mining before mining happens. These processes include: Business Research: Before we begin the process of data mining, we must have a complete understanding of the business problem, business objectives, the resources available plus the existing scenario to meet these requirements. Having a fair knowledge of these topics would help to create a detailed data mining plan that meets the goals set up by the business. Data Quality Checks: Once we have all the data collected, we must check the data so that there are no blockages in the data integration process. The quality assurance helps to detect any core irregularities in the data like missing data interpolation. Data Cleaning: A vital process, data cleaning costumes a considerable amount of time in the selection, formatting, and anonymization of data. Data Transformation: Once data cleaning completes, the next process involves data transformation. It comprises of five stages comprising, data smoothing, data summary, data generalization, data normalization and data attribute construction. Data Modelling: In this process, several mathematical models are implemented in the dataset. What are the techniques of data mining? Association: Association (or the relation technique) is the most used data mining technique. In this technique, the transaction and the relationship between the items are used to discover a pattern. Association is used for market basket analysis which is done to identify all those products which customer buy together. An example of this is a department store, where we find those goods close to each other, which the customers generally buy together, like bread, butter, jam, eggs. Clustering: Clustering technique involves the creation of a meaningful object with common characteristics. An example of this is the placement of books in the library in a way that a similar category of books is there on the same shelf. Classification: As the name suggests, the classification technique helps the user to classify and variable in the dataset into pre-defined groups and classes. It uses linear programming, statistics, decision tree and artificial neural networks. Through the classification technique, we can develop software that can be modelled so that data can be classified into different classes. Prediction: Prediction techniques help to identify the dependent and the independent variables. Based on the past sales data, a business can use this technique to identify how the business would do in the future. It can help the user to determine whether the business would make a profit or not. Sequential Pattern: In this technique, the transaction data is used and though this data, the user identifies similar trends, pattern, and events over a period. An example is the historical sales data which a department store pulls out to identify the items in the store which customer purchases together at different times of the year. Applications of data mining Data mining techniques find their applications across a broad range of industries. Some of the applications are listed below: Healthcare Education Customer Relationship Management Manufacturing Market Basket Analysis Finance and Banking Insurance Fraud Detection Monitoring Pattern Classification Data Mining Tools Data mining aims to find out the hidden, valid and all possible patterns in a large dataset. In this process, there are several tools available in the market that helps in data mining. Below is a list of ten of the most widely used data mining tools: SAS Data mining Teradata R-Programing Board Dundas Inetsoft H3O Qlik RapidMiner Oracle BI
Data Warehousing
What is data warehousing? Data warehousing is defined as the method of gathering & handling data from different sources to get meaningful output and insights. Data warehousing is central to the BI system and is built for data analysis and reporting. Source: © nfo40555 | Megapixl.com In simple terms, a data warehouse is a large collection of data utilized by businesses to make investment decisions. What are the characteristics of data warehousing? Data warehouse has supported businesses in making informed decisions efficiently. Some of its key features are highlighted below: The data in a data warehouse is structured for easy access, and there is high-speed query performance. The end users generally look for high speed and faster response time – two features present in data warehousing. Large amount of historical data is used. Data warehouse provides a large amount of data for a particular query. The data load comprises various sources & transformations. What are the benefits of data warehousing? The Companies which used data warehousing for analytics and business intelligence found several advantages. Below are some of them: Better Data: When data sources are linked to a data warehouse, the Company can collect consistent and relevant data from the source. Also, the user would not have to worry about the consistency and accessibility of the data. Thus, it ensures data quality and integrity for sound decision making. Faster decisions: Through data warehousing, it is possible to make quicker decisions as the data available is in a consistent format. It offers analytical power and a comprehensive dataset to base decisions on tough truths. Thus, the people involved in decision making do not have to rely on hunches, incomplete data, and poor quality data. It also reduces the risk of delivering slow and inaccurate data. How does a data warehouse work? A data warehouse is like a central repository where the data comes from various sources. The data streams into the data warehouse from the transactional system and other relational databases. These data could either be structured, semi-structured or unstructured. These data get processed, altered, and consumed in a way that the end-user can gain access to the processed data in the data warehouse via business intelligence (BI) devices, SQL clients and spreadsheets. A data warehouse merges the data that comes from various sources into a complete database. The biggest advantage of this merged data is that the Company can analyze the data more holistically. It also makes the process of data mining smooth. Copyright © 2021 Kalkine Media Pty Ltd. Component of a data warehouse A data warehouse can be divided into four components. These are: Load Manager Load Manager, also known as the front component, does operations related to the mining and loading the data into a data warehouse. Load manager transforms the data for entering into Data warehouse. Warehouse Manager The warehouse manager manages the data within the data warehouse. It analyses data to confirm that the data in the data warehouse is steady. It also conducts operations such as the creation of indexes and views, generation of denormalization and aggregations, modifying and integrating the source data. Query Manager Query Manager is a backend component that does operations concerning the supervision of user queries. End-User access tools End-User access tools comprise data reporting, query tools, application development tools, EIS tools, data mining tools, and OLAP tools. Roles of Data Warehouse Tools and Utilities The tools and utilities in a data warehouse are used for: Data extraction: The data extraction process involves gathering data from heterogeneous sources. Data cleaning: Data cleaning consists of searching for any error in the data. Data transformation: Data transformation process involves changing the data into a data warehouse setup. Data loading: This process involves data sorting, recapping, consolidating, verifying integrity. Refreshing: This process requires revising data sources to the warehouse. Application of data warehouse Data warehouse plays a considerable role across multiple sectors. Some of the sectors it caters to are highlighted below. Aviation sector In the aviation sector, a data warehouse’s role can be seen in crew assignment, route profitability analysis, any promotional activity. Banking Industry In the banking sector, the focus is on risk management, policy reversal, customer data analysis, market trends, government rules and regulations and making financial decision. Through a data warehouse, banks can manage the resources available on the deck effectively. Banks also take the help of a data warehouse to do market research, analyze the products they offer, develop marketing programs. Retail industry Retailers act as an intermediary between the producers and the customers. Hence, these retailers use a data warehouse to maintain the records of both producers as well as the customer to maintain their existence in the market. Data warehouses help track inventory, advertisement promotions, tracking customer buying trends and many more. Healthcare industry In the healthcare industry, a data warehouse is used to predict the outcome of any test and taking relevant action accordingly. Data warehouses help them to generate patient treatment report, offer medical services, track the medicine inventory. Many patients visiting hospital have health insurance. Through a data warehouse, hospitals maintain the list of insurance providers. Investment and insurance sector In the insurance and investment sector, the role of data warehouse becomes important in tracking the data pattern, customer trend and market movement. Services sector In the services sector, a data warehouse is used for maintaining financial records, studying the revenue pattern, customer profiling, resource management and human resource management. Telecom The telecom sector uses a data warehouse in the promotion of its offerings, making sales decision, distribution decision, features to include in case they decide to launch a new product based on the customer requirement. Hospitality The hospitality sector involves hotel and restaurant services, car rental services etc. In this sector, the companies use a data warehouse to study the customer feedback on the various services offered and accordingly design and evaluate their advertising and promotion campaigns.
FAANG Stocks
In 2013, the television host of CNBC's Mad Money, Mr Jim Cramer addressed few stocks as “totally dominant in their markets”. He was referring to tech titans and named them FAANG stocks (where the extra “A” was added 5 years later, in 2017). ALSO READ: Investment in Technology Stocks - A Beginner's Guide What Are FAANG Stocks? “FAANG” is perhaps one of the most popular abbreviation of the business world. The acronym illustrates stocks of the famous five US-based technology corporations- first being social media giant Facebook Inc., followed by software and hardware developer Apple Inc., the e-commerce magnate Amazon.com Inc., and the streaming service provider Netflix Inc., along with the last FAANG member, internet ace Alphabet Inc. (formerly recognised as Google). Originally, the acronym was FANG (with an “A” for Amazon.). In 2017, investors included Apple in the group, turning the acronym into FAANG. There is an interesting fact here- The original four FANG stocks were pure internet-based companies, but the later inclusion of Apple, that is a consumer hardware manufacturer, made FAANG stocks a broader group of giant technology stocks. Widely renowned among consumers, unique in their products and services, these stocks are of few of the largest companies in the world. They trade on the NASDAQ Exchange and are included in the S&P 500 Index, making up approximately 15 per cent of the index. Market experts believe that since these stocks have a large influence on the index, they tend to have a substantial effect on the performance of the S&P 500, in general. GOOD READ: FAANGs Defining Resilience Amid Market Downtrends Why Are FAANG Stocks Popular? FAANG companies exhibit several competitive advantages that make them attractive long-term investments. Consider this- Facebook rules social networking, Amazon is the one-stop destination to buy goods online in today’s digital world; Apple’s iPhones are one of the most used and well-renowned gadgets globally; Netflix is considered to be a leader of online streaming; whereas Google is the search engine used comprehensively almost every day, everywhere. These disruptive companies benefit from what is known as the network effect (indirect value goods and services gain as more people use them). Facebook’s products are valuable to new users because of its vast other active users. Amazon’s Prime service brings millions of shoppers to its marketplace every day, making its seller services more attractive to third-party merchants. Millions of Netflix viewers provide feedback for the kind of content the company should invest in. Lock-in effect of the Apple ecosystem creates substantial switching costs for iOS users. FAANG companies have intangible assets. This opens doors to the possibility of producing higher levels of profitability than rival companies. Consider this- Facebook, Amazon, and Google have troves of user data to pursue advertisements. Netflix offers original content, exclusive licenses that make its content library unique. Apple, on the other hand, is one of the few technology companies that makes hardware as well as software for its devices. FAANG players contribute to radical lifestyle change. One obvious reason for the popularity of these market darlings is that each FAANG company has been known to transform not just their own industries and the markets, but also how we all live in the current contemporary lifestyles. What is the significance of FAANG Constituents? As the heavy weighting of FAANG stocks in indexes like the S&P 500 gives them an outsized impact on the broader stock market, it seems worthwhile for investors to learn a bit about them. How is Investing Community Exposed to FAANG Stocks? FAANG stocks have historically outperformed the S&P 500 index. Over the last decade, this famous group accounted for a large portion of the market’s gains and American economy growth. This seems obvious given that FAANG companies have a hoard of competitive advantages making them seem like lucrative long-term investments. Offering perhaps the hottest technology trends, FAANG stocks demonstrate strong sales and earnings growth. Each FAANG company is listed on the NASDAQ, so purchasing their shares is a straightforward process for most investors. The easiest path could possibly be via online brokerage account with companies that offer this service. At this point, it should be noted that FAANG stocks aren’t cheap. For instance, for most of 2019, one share of Google sold for well over USD 1,000 and Amazon traded above USD 1,500. However, a wise investor knows that past results do not guarantee future success. Sinusoidal equity market trends deserve closer attention to a lot of other aspects before making any investment decision. Therefore, investing in FAANG stocks should be vigilantly based on one’s research of fundamental and technical aspects and risk appetite. GOOD READ: Investing Tips: 4 Reasons Big Techs can always stay your best pal Are There Any Risks Associated to Investing in FAANG Stocks? Market experts believe that there are no sure plays in the investing world. Simply put, there is a risk in every aspect of investing. Though favourable market conditions and investor enthusiasm for technology seems to be here for good, global uncertainties always should be considered. Overly bullish expectations coupled with certain political pressures and economic worries may hinder these big techs’ growth. Some experts opine that as these companies continue to mature amid mounting worldwide risks, it may get increasingly difficult for them to maintain their rapid growth pace. Legal Regulatory, market and operational risks of these FAANG players need to be considered before taking any exposure to FAANG stocks. Amazon and Google have often come under regulatory examination for potential anti-competitive business practices. Facebook and Google have faced criticism for lack of data privacy and security. On the other hand, Netflix has encountered new competitors in streaming video and as few reports suggest, a huge debt load linked with content production. Valuations of FAANG players should be well justified viz-a-viz earnings guidance of these players, before taking any investment exposure. Are There Global Peers to FAANG Stocks? Just like FAANG stocks, there are several groups of companies that can be looked upon as peers to the tech group. Let us cast an eye on similar groups- The Australian variant, WAAAX stocks comprises WiseTech Global Limited (ASX:WTC), Appen Limited (ASX:APX), Altium Limited (ASX:ALU), Afterpay Limited (ASX:APT) and Xero Limited (ASX:XRO). GAFAM is an acronym for the five most popular US. tech stocks: Google, Apple, Facebook, Amazon, and Microsoft. BATX is the abbreviation for the four popular technology stocks from China: Baidu, Alibaba, Tencent and Xiaomi. TAND, which comprise of Tesla, Activision, Nvidia and Disney are often looked up as future giants of tech. TANJ stocks in Hong Kong comprise Tencent, Alibaba, NetEase and JD.com. The Canadian big tech club DOCKS constitutes Descartes Systems, Open Text, Constellation Software, Kinaxis and Shopify. Do You Know These Interesting Facts About FAANG Stocks? The FAANG group has been a stock market superstar on both short and long-term basis. These stocks have more or less consistently delivered above-average sales and profit growth and maintained juicy margins. Let us look at a few interesting facts about these tech titans- In August 2018, FAANG stocks were responsible for nearly 40 per cent of the S&P 500’s gain from the lows reached in February 2018. Over the past decade, FAANG stocks have grown faster than the overall S&P 500 or the more technology-focused NASDAQ. There is no exchange traded fund dedicated solely to FAANG stocks. Since the market bottom in March of 2009, the worst performing FAANG stock, Apple, has returned over double that of the index average. Amid the COVID-19 market downturn FAANG companies were one of the biggest beneficiaries as the “stay-at-home” economy led to an acceleration in their trajectories as people’s lives shifted online. Rather than resting on their achievements and dominant market position, FAANG companies choose to use their cash on hand to make investments in cloud computing, AI and other technologies that they believe may lead to continued revenue growth.
Capital IQ
Capital IQ, designed by Standard Poor's (SP), is a market intelligence platform, which provides research, data, and analysis on private and public companies to stakeholders such as finance professionals to perform some analysis or even support transactions like acquisitions and mergers.
