Significance of Artificial Intelligence and its Benefits for Mankind

Despite the implications and various industries adopting Artificial Intelligence (AI) applications, some people remain unfamiliar with the concept. AI has touched almost every aspect of human life and has become an integral part of societies and businesses. Artificial Intelligence seed investors have worked tirelessly to bring new ideas and augmentations to reality. These implications have improved human capabilities beyond measure and given rise to efficient production for every sector. In short, we cannot imagine life without AI intertwined in everything. AI has become a centrepiece for the 4th generation industrial revolution. This revolution has questions about the challenges we perceive as humans and have a much more transformative ability than any other industrial process.

Importance of AI

With some extent of unfamiliarity still present, every AI Company works towards implementing technology to transform the lifestyles of many. AI has become a useful tool that makes us rethink how we integrate, analyze and use the data. The resulting insights can make predictions for better decision making. The novel applications of AI in finance, security, health care, and other areas address data access and transparency problems. To maximize the benefits offered by Artificial Intelligence companies, these steps might ensure the importance in our lives need consideration:

  • Greater access to data for researchers without the danger of user privacy
  • Investment of Artificial Intelligence seed funding
  • Encouraging the application of new models for teaching skills to the AI workforce
  • Promote policy recommendations using a federal AI committee and engaging with local officials
  • Regulating AI principles along with algorithms
  • Taking complaints seriously and implementing solutions accordingly
  • Maintain control for human oversight by promoting cybersecurity and penalizing suspicious AI behavior



AI Helping Mankind

Along with various applications that have made our lives easy, every AI Company is also working towards developing systems that can help humanity progress. Some of these beneficial aspects in which AI can help humans include:

  • Eradicating Poverty

The same technology that posts customized ads on our social media also holds the key to end the worldwide poverty crisis. One of the biggest hurdles in controlling poverty is a lack of knowledge about where it occurs. Countries conduct surveys to spot low areas, but that amount of data collection is slow and difficult for underdeveloped countries. However, with the applications developed by any Artificial Intelligence company dealing with analysis and forecasting, it can gather complex data and pinpoint low visible spots. Governments can design policies that can solve the issue of poverty.

  • Education

Artificial intelligence is the key to improving educational aspects by relieving teachers’ access burden and giving students access to a meaningful education. Students have access to learning mechanisms that can assist in studies. Young children living in rural areas have limited access to educational institutes because of transportation or other challenges. AI applications can help them receive an education on par with other children. Artificial Intelligence seed companies have taken full measures to ensure that the applications and startups concentrate on educational aspects. AI also offers a significant advantage for teachers by simulating their presence for round-the-clock access. Students have the availability of a teacher’s curriculum without having physically present at a point. This would free up resources to concentrate on other activities that can make education better.

  • Safe Transportation

Transportation and traffic control is also one of the chief problems of today that require the utmost focus. The hectic transportation issues lie because of a lack of sophisticated systems that can analyze and reroute traffic choke points.

Any AI company in line with state command and control facility can design systems that can automate public transportation while keeping the roads clear at all times. This will eliminate the issues of unexpected traffic jams within a state and provide timely transports for people.



Final Word

With the development in AI systems, the applications have offered significant benefits for prospects. While some implications may seem far off, many have started to consider solutions for daily problems. Implementation of automated programs has made sure that Artificial Intelligence seed companies invest heavily in the right technology to better humanity.

Smart Ways to Use Artificial Intelligence for Marketers

When the idea of Artificial Intelligence comes into focus, we immediately create an image of machines taking control and making humans their slaves. The perception of AI can be either beautiful or scary, depending on the thinking. However, we cannot deny that Ai has become an integral part of today’s market, and various industries have started to reap the benefits. AI seed programs and startup incubation have invested heavily in bringing AI to mainstream markets.

Businesses need customers to remain functional and utilize various marketing techniques to attract them. Marketers look for creative ways to reach new customers and maintain existing clientele. With the latest digital marketing concepts replacing traditional techniques, tech-savvy marketers use every application they can muster to make their marketing efforts a success. Enter Artificial Intelligence in marketing, a new wave of data-driven marketing trend taking the world by storm. Artificial intelligence brings in highly customized experience for customers that costs less than traditional high-value marketing campaigns. The most significant benefit is that every interaction with a customer or prospect provides a future optimization for the product or service.

As AI seed programs saw the potential of using artificial intelligence and machine learning in marketing, they encouraged marketers to plan their campaigns accordingly. In the last few years, AI has paved the way for brands to enhance every step of the buyer’s journey. Tools previously only available to corporate enterprises are now accessible for small and medium businesses. In short, AI has made marketing efforts more streamlined and targeted.

Artificial Intelligence in Marketing

The impacts of AI in marketing strategies have tremendous results. Studies show that more than 55% of industries have implemented AI for their marketing practices. The reason is that AI helps marketers to have market data analysis gathered from social media, web, and other platforms. AI seed investors bring the technology to mainstream marketing in the hopes of gathering better insights in a relatively short time. These insights boost performance and get a faster return on investment (ROI).

Another reason is that customers expect that companies understand their expectations and demands. Artificial Intelligence in marketing guides marketers to make campaigns that revolve around the needs analyzed detailed market data.

To understand AI’s effectiveness in marketing, let us discuss some smart ways marketers utilize the system for successful campaigns.


  • Sales Forecasting

Forecasting sales is the primary point that makes a marketing strategy successful and monetizing. Combining the expertise of AI seed and marketers, data gathered through past deals give valuable insight. AI and machine learning algorithms than study the data to analyze the patterns and develop sales forecasts. Marketers can use that prediction to shape their current and future campaigns for better performance and expected results.

  • Deeper Customer Understanding

Marketers have a deeper understanding of customer demands, thinking, and feeling in real-time through AI solutions. With the data available on social media and web and past interactions with clients, AI creates a better customer persona that marketers can use to target specific demographics. Responsive marketers harness the power of the data to modify the campaign according to changing feelings promptly.

  • Optimizing Digital Marketing

With a vast majority of users having an online presence, marketers miss a great deal if they steer away from digital marketing efforts. AI seed programs suggest that using machine-learning aspects in digital marketing will better target demographics with better customer experience. Digital marketing platforms like SEO, PPC, and Social Media depend on buyer personas to generate leads and ultimately paying customers. In this way, AI performs an automated optimization of online marketing strategies that fits the hour’s demands.

  • Content Creation

For any successful marketing campaign, content plays a vital role in targeting the relevant prospects. AI seed programs give better data windows for marketers to create content that revolves around the solutions customers need. Manual content generation is a tedious process, but with AI predictions, marketers have patterns that allow them to make customers’ content. Engagement with content reveals much about customers’ needs and demands that marketers can use for targeting prospects.

Final Word

As AI seed investors continue to bring artificial intelligence into the mainstream market, today’s marketers have started to take advantage of the system. The sophisticated machine learning protocols provide marketers with enough forecast to shape their campaigns accordingly.

Understanding the Relationship of Big Data and Artificial Intelligence

Big data and Artificial intelligence are two of the most popular and widely recognized technologies today. Artificial intelligence has existed for more than a decade, while big data came into focus a few years back. Computers today store millions of records, and Big Data technology gives them the power to analyze that stored record. The world already entered the phenomenon of big data before coining the terms and investments made by AI seed companies. By the time Big Data made headlines, massive amounts of data, already stored and analyzed, provided insights about the industry that owns the data.

IT professionals worldwide quickly realized the need for sophisticated algorithms to handle such large-scale analysis. Artificial intelligence came to the rescue and improved decision-making processes that were too much to run for the human mind. Artificial intelligence algorithms have offered written scripts to accomplish the task of analysis and deriving results.

Big Data and Artificial Intelligence

The reciprocal relationship between Big Data and Artificial intelligence has come so close that it seems impossible to separate the two. Various AI seed companies and IT professionals explain the relationship as the combination that integrates, accesses, and reports all the available and co-related data for achievable insights.

Today, companies and industries have started to use AI and big data on an increasing level. They access the IT resources harvested from data centers and then apply available AI tools such as cloud computing to analyze the Big Data collected. Some common ways in which AI tools support Big Data analysis include:

  • Detection of Anomalies

AI algorithms analyze Big Data to detect and identify anomalies or unusual occurrences present in the collected data set. A network of parameters and sensors that have a defined range mostly benefits from such an application. Any node that comes outside of the data range comes under-identification as an anomaly and reported immediately.

  • Probability of Future Results

According to AI seed experts and IT professionals, artificial intelligence uses Bayes theorem for supporting Big Data analysis. The system determines the likelihood of occurring events using known conditions that have a probability of affecting any future outcome.

  • Patterns in Data Set

AI identifies patterns in Big Data set that might remain undetected by the human eye. It also detects changes in bars and graphs made within the underlying data set.

AI and Big Data are the two giants as considered by data scientists and IT corporations. Many think that the combination of AI and Big Data will be a revolution in their organization and change the market dimensions drastically. According to AI seed experts, machine learning is the advanced version of artificial intelligence that supports sending and receiving data. At the same time, it also learns new concepts to analyze that data for meaningful insights and predictions. Big Data gives AI the chance to explore the data and draw conclusions for organizational benefit.

Big Data Helping AI Experiments

AI seed programs remain on the lookout for AI experiments that can benefit the world and change its performance for good. As a common thought, most people believe that AI will take over human intervention through machine learning capabilities, and the social role will become only that of an implementer. Big Data has broken and changed this idea by showing that machine learning cannot begin without any data to support its readability. Machines can make decisions based on facts present but cannot give a touch of emotional interaction. Big Data provides data scientists a way to inject human emotions in the decision made by machines. The involvement of emotional intelligence supports devices in making a better decision in the right manner.

For AI seed supporters and data scientists, the combination of AI and Big Data gives them a chance to analyze the customer needs according to local market regulations. Depending on the collected analysis, experts can make an informed suggestion that can influence the market. Machine learning, although very sophisticated, cannot make such analysis without the presence of Big Data.

As data scientists, we can see that the mix of AI and Big Data not only involves simultaneous learning; it also offers new concepts for the market. Businesses know their customers well through a mixed analysis and learned concepts.

AI and Big Data have connected permanently, and the combined usage will grow eventually. The trend has a substantial value of AI analytics applied to Big Data that AI seed and IT experts can use for rapid expansion.

Guide to Deep Learning for Beginners

Deep learning has become a commonly used term for many AI developers these days. It is a complex subject that many data scientists spend their lifetime perfecting. For many who want to switch careers and become data scientists or have a little knowledge about artificial intelligence (AI), deep learning is a difficult subject to dive in to. This guide is designed for those who want to spark their interest in deep learning and introduce themselves to the basics.

What is Deep Learning

Deep learning (also known as deep machine learning) is a category of machine learning based on algorithms that attempts to model high-level abstractions in data. In simple terms, an AI program based on deep learning has one neuron that receives an input signal and another that sends output signals. An input layer receives an input and passes an updated version of that input to the next layer. In deep learning, many layers are present between input and output that allows algorithms to utilise multiple processing layers. All these layers have various linear and non-linear transformations.

Types of Deep Learning

For those new to AI programming using deep learning algorithms, there are three categories of deep learning model:

  1. Supervised Learning

In this type of group, the machine has a supervisor that provides it with answers. Data is already labelled, and the machine uses these labels and examples to learn, the results of which can be applied to future cases. An example is made up of input (vector) and output object (the supervisor). The learning of algorithm is picked from the labeled training data and then produces an inferred function used to map new objectives. This is the most common type preferred by AI programmers and developers. Classification tasks are most dependent on supervised learning that includes:

  • Identification of objects in images
  • Speech and language recognition
  • Spam detection and analysis of sentiments


  1. Semi-Supervised Learning

Semi-supervised learning is the learning made from labeled information combined with what has been observed from unlabeled information. An example of this type is a child growing up while learning from the parent (labelled information) and combining it with their observations like house, trees, roads (unlabelled data). AI programming based on semi-supervised learning uses both labelled and unlabelled data for training in which the majority of data is unlabeled with a few instances of labelled data. Semi-supervised learning can be classified as:

  • Transductive: Infers the correct labels for the given data, or
  • Inductive: Infers correct mapping from point one to another


  1. Un-Supervised Learning

Un-supervised in deep learning, AI programming is when machines learn the relationship between the elements in a dataset on their own. The data is then classified without the help of labels, and the algorithm looks for patterns or hidden features to analyse the data. Most common algorithms include:

  • Anomaly detection
  • Clustering
  • Neural networking


The Architecture of Deep Learning

  1. Generative Deep Architecture: This architecture is intended to characterize the high order correlation properties in observed data to analyse the patterns. It also describes the joint distributions of visible data and associated classes.
  2. Discriminative Deep Structure: It provides direct discriminative power for pattern classification and often characterises on prior distributions of classes of visible data.
  3. Hybrid Structures: In AI development, the goal here is to discriminate and assist the outcome of generative structure with better optimisation and regulation.

Deep Learning or Machine Learning

In AI development, deep learning is a subset of machine learning. People often get confused about using one over the other. When it comes to large datasets, deep learning works best, while machine learning is mostly used for small datasets. Another difference is that deep learning algorithms need time to train due to the broad parameters. On the other hand, machine-learning algorithms can train within a few hours. Interpreting ability is also a reason companies use machine learning in their AI programming. 

Final Word

In our world, an estimated 1 quintillion bytes of data are presently generated every day. As the information grows, so does the aspect of deep learning. The resulting neural networks can analyse and discover patterns within the amount of unstructured data present. If you’d like to gain experience while working towards your PhD in Computer Science, keep in mind that AI Seed offers helps place such PhDs in AI or machine learning startups.

Language Modeling: Types and Importance in Artificial Intelligence

What is Language Modeling

Language modelling or LM is the use of various statistical techniques to determine the probability of a sequence of words in a sentence. The purpose of language models is to analyse the bodies of text data to provide a basis for their word predictions. Companies like AI Seed use this language modelling in applications for Natural Language Processing (NLP). Some of the most common applications include machine translation and question answering. 

Types of Language Modeling

  1. Statistical Language Modeling

As its name suggests, statistical language modelling means the development of probability models that predict the next work on the sequence given in the preceding words. Based on the examples of text, the language model determines and predicts the probability of word occurrence. The simple model works on the context of a short sequence of words, whereas complex models work on entire sentences or paragraphs. However, commonly speaking, language models work on short word sequences mostly. A language model can be developed and applied as a standalone application, such as generating new words that appear from previous ones. Language modelling serves as the root problem for a range of natural processing tasks.


The practical use of language models is in front-end or back-end of a more sophisticated model that requires an understanding of language. An excellent example of language modelling is in speech recognition programs developed by companies like AI Seed, where audio data is used as an input to the model. The output required a language model to interpret the voice signals. This model then analyses these signals to predict the new word in the context of the words already recognised. Similarly, language modelling is used to determine and generate text in various natural language processing tasks. Some of the most common ones include:

  • Image Captioning
  • Machine Translation
  • Spelling correction
  • Optical character and handwriting recognition
  • Text summarisation and many others


  1. Neural Language Models

Neural language modeling has become popular in recent years and is anticipated to become the preferred approach for machine learning. The use of neural networks in machine language models is called Neural Language Modeling or NLM for short. Neural network-based models have garnered better results than traditional methods both in standalone and incorporation with larger models. These models are most widely preferred to handle complex tasks like speech recognition and machine learning. The key reason companies like AI Seed adopt this model is the model’s ability to generalise. This model assumes word embedding in a real-valued vector setting representing each word in a projected vector space.The representation of words based on their use allows the model to give similar representation to words having the same meaning. The generalising nature of this model makes it more efficient than the statistical model. Furthermore, the distributed representation ability allows the word embedding to be represented better with the size of the vocabulary. The three properties based on which a neural model works are:


  • Association of words in the vocabulary with a distributed feature vector
  • Express joint probability functions of word sequence for feature vectors
  • Learn the feature vector and parameters of probability function simultaneously

Importance of Language Modeling

Language modelling is a crucial part of modern NLP applications and is why machines understand qualitative information. In one way or another, each model turns qualitative information in quantitative for better prediction and understanding. This allows the communication of people with machines as they do with each other to a limited extent. Companies like AI Seed develop programs for a variety of industries like tech, finance, healthcare, and transportation, legal, military, and government that use machine language modelling. Today, it is likely that a person interacts with machines in some way, whether it is Google search, auto-correct function, or voice assistant.



Language modeling applications include the following:

  • Speech recognition where machines process speech or audio data. Typical examples are Alexa and Siri.
  • Translation of one machine language to others. Examples include Google Translate and Microsoft Translate.
  • Characterisation and markup of different words by grammatical characteristics for parts-of-speech recognition.
  • Recognise sentiment behind a word or phrase to understand opinions and attitudes behind that text.

Analysis of any string of data or sentence that converts to formal grammar and syntax rules.

The Difference Between Machine Learning and Artificial Intelligence

Machine learning and artificial intelligence (AI) are two parts of computer science that are almost co-related to each other. These two technologies are favoured by companies to create smart programs for others to use. AI Seed offers investment support to machine learning and AI startups. In 2020, people benefit from many AI-powered programs like Uber, Google Maps, and many other applications. However, there is some confusion in what consists of machine learning and how it differs from artificial intelligence. They are not exactly the same, but the perception that they are can lead to confusion.

Both terms come up in on a day-to-day basis when it comes to Big Data analysis, and the broader aspects of technological change sweeping through our world. The short answer to both these terms is:

Practical: Machine learning and artificial intelligence are interchangeable terms and used in supervised learning.

Theoretical: Machine learning is a branch of AI or is a way of implementing AI.

Although both are related technologies that people sometimes use as a synonym for each other, there are differences between them.

Below are some significant differences between these two terms, along with an overview of both:

Artificial Intelligence

The word artificial intelligence is made up of two words “artificial” and “intelligence.” Artificial refers to anything made by human hands or non-natural thins, and intelligence means the ability to understand and think. A common misconception is that AI development is a system, but in fact, it is not a system rather an implementation of it. One of the most explicit definitions of AI is the ability granted to computers to think and make decisions on their own better than present humans do. Hence, AI is the intelligence where we want to add all the capabilities of humans in machines.

Machine Learning

Machine learning enables a system to make predictions based on data and is a valuable part of AI development systems. It takes data as information to make predictions without being explicitly programmed. Machine learning utilizes a large amount of structured or semi-structured data to produce accurate results or forecasts. The machine learning model has self-learning algorithms that make use of historical data and works in specific domains. Machine learning is used in a number of applications like email spam filters, Google search algorithms, auto friend tagging in Facebook, etc.


Major Differences between Machine Learning and Artificial Intelligence

Machine Learning

Artificial Intelligence

Machine learning is a subset of AI that uses historical data for prediction without programming AI technology mimics human intelligence and simulates human behaviour
Aim of machine learning is to learn from past data to give an accurate result Aim of AI is to make a smart system like humans to solve complex problems
In machine learning, humans teach machines to use data to generate results AI make intelligent systems that think like humans
Deep learning is a subset of machine learning Machine learning and deep learning are subsets of AI
Machine learning allows the system to learn new things from data provided AI system makes a decision on its own
It involves the creation of self-learning algorithms It is the development of a human mimicking system to respond in different scenarios.
Machine learning goes for just a solution, not discerning whether it is optimal or not Artificial intelligence goes for optimal solutions
Machine learning leads to knowledge Artificial intelligence leads to wisdom
Machine learning is concerned with accuracy and patterns Artificial intelligence is concerned with maximizing chances of success
Primary applications of machine learning are Google search algorithms, Facebook auto friend tagging, etc. Primary applications of AI programs are SIRI, online gaming platforms, humanoid robots, and automatic chatbots,
Machine learning can be divided into supervised, unsupervised and reinforced learning Artificial intelligencg can be divided into weak AI, strong AI, and general AI
Machine learning deals with only structured and semi-structured data AI deals with structured, semi-structured and unstructured data


Final Word

The points as mentioned above are some of the key differences between these two terms. While machine learning and AI programs may look one and the same, these differences show that there are details that make them change from one other. However, both these technologies have become a significant aspect of today’s innovation and developmental issues. AI Seed offers investment support to machine learning and AI startups; contact us today if you would like AI PhDs to join your startup, or need a workspace at a subsidised rate, or want to find brilliant minds to work for you, etc.

Beginners Guide to Machine Learning

As long as new technologies and innovations have risen, there is always a difficulty in its adaptation. Eventually, we come to appreciate these new inventions even if we have some skepticism at the beginning. Yet when it comes to computers, there is still some sort of anxiety present with the adaptability and usage. We love computers, and in fact, we all have one in our pockets all the time – a smartphone. We have become so dependent on technology that thought of a world without them sends waves of anxiety through us. However, taking all the positive aspects that innovations in computers have provided us, we now know that machine learning and artificial intelligence are paving the way for a new world. Companies like AI Seed have invested heavily in developing machine learning and AI programs to make life easy for us.

Learning is a necessary process for anyone defined as the ability to acquire knowledge and skills through experience, study, or being taught. The commonality of learning is what separates us humans from the rest of the living beings on this planet. In this day and age, we have means, the resources to teach machines to learn; some even learn on their own. This is called machine learning. In this brief guide, we will give beginners insights into machine learning and artificial intelligence.

What is Machine Learning

Machine learning involves computer systems to recognize patterns by examples found in data rather than pre-setting the rules through programming. Machine learning is all about algorithms or set of rules to learn from complex functions (patterns) from data to make precise predictions. It has three necessary steps in which development works:

  • Taking some data
  • Finding patterns in that data
  • Making predictions based on those patterns

How Machines Learn

Simply put, machines learn by recognizing patterns in similar data. Earlier, programmers would tap the outline of codes to instruct a system on what it needed to do. Advanced development by companies like AI Seed lets machines decide what they want to do. Machine learning is now far more accurate and rivals that of a human mind. They take data as information from the world and become smarter as more data is uploaded to them.

But, not all data is the same: the information provided can lead a machine to pick a particular direction. The better the information provided, the more the uncertainty is reduced, and the more accurate the results. So, it is essential to pay attention to the type of data a machine is given to learn. After a sufficient amount of data is uploaded, the machine begins to make predictions more accurately. Machines can make predictions about the future, too – as long as it’s not different from the past. AI Seed companies use machine-learning aspects by extracting information from old data to predict the likelihood of something happening.

Types of Machine Learning

As the focus of the field is learning, there are many types associated with machine learning. AI Seed companies use a specific kind of machine learning based on development requirements. Based on their learning nature, these types are divided into sub-types as well:

  • Type 1: Learning Problem

Supervised Learning: Describes a class of problems that involve using a model to learn a pattern between input examples and targeted values.

Un-supervised: Describes using a model to extract relationships in data.

Reinforced: Describes the type of problems in which a user learns to operate in an environment and to use feedback.

  • Type 2: Hybrid Learning Problems

Semi-Supervised: Learning where training data contains very few labelled examples and many unlabelled ones.

Self-supervised: This is a learning problem framed as supervised to apply specific algorithms to it.

Multi-Instance: Learning where individual examples are not labelled, and everything else is labeled.

  • Type 3: Statistical Inference

Inductive Learning: Uses evidence to determine outcomes.

Deductive: Refers to the use of specified rules to determine the outcome.

Transductive: Refers to the theory of predicting specific examples given from a particular domain of instance.

  • Type 4: Learning Techniques

Multi-task Learning: Involves fitting a model on a dataset that addresses multiple problems.

Active Learning: A technique where a machine can question a user or an operator to resolve any complexity.

Online: Involves updating data directly before making a prediction based on available data.

Transfer: Learning where a model first trained in one task and then used as a starting point for other related tasks.

Ensemble: A learning approach where two or more learning models use the same data and predictions from each model is combined.

Why 2020 is the year for an AI startup

We’re on the verge of something really big, which is AI. It is going to be the biggest change in human history, even more than electricity. Artificial intelligence is impacting the imminent future of virtually every industry, along with every human being. Artificial intelligence has acted as the primary driver of many renowned emerging technologies like Robotics, big data, and IoT, and it’ll continue to act as the front runner for all technological innovations for the foreseeable future.

Where it all started

AI seems like a new concept, but on the contrary, it has been around since 1956, when an MIT professor John McCarthy coined the term

According to McCarthy

“every aspect of knowledge or any feature of intelligence can, in principle, be so precisely defined that a simple machine can be made to simulate it.”

What is AI?

Artificial intelligence AI SEED is the replication of human intelligence processes by simple or complex machines, especially computer systems. AI is an interdisciplinary science with various approaches, but recent advancements in deep learning and machine are creating a paradigm shift in every sector of the tech industry. Specific applications of AI include natural language processing (NPL), expert systems, machine vision, and speech recognition.

Three main reasons why 2020 is the year for you to start an Artificial Intelligence startup

Advent of Digital Economy

The Digital Economy has reshaped our lives primarily due to the popularisation of smartphones, as they generate billions of data points that several companies are now turning into learnings. Thus, a huge amount of quality data is available to train models.

Increased computing power

Today the cost of running AI algorithms on servers has severely decreased. It has never been so inexpensive and easy to train mathematical models.

Quantum leaps in Deep learning

The world’s biggest tech companies, including the big 4, which includes Apple, Google, Facebook, and Amazon, have heavily invested in AI either by hiring world-class AI researchers or through acquisitions. And they’ve also published parts of their codes and research so everyone could use them.

AI is everywhere

We might not know it, but AI is present everywhere, when Netflix recommends us to watch another series, it does that through an algorithm which observes our preferences and advises us to watch something similar. This is just one of many examples where AI is at work

AI is evolving into intelligent assistants that help us work more efficiently. They are also used in drones to deliver the things we get on Amazon, and how can we not mention Tesla’s self-driving car that runs on the most advanced AI.

AI has come a long way in Speech recognition technology. It doesn’t matter how you talk, fast or slow, the AI will understand what you are saying, and you don’t need to learn specific voice commands, you can normally talk to the machine as if you’re talking to a friend and they’ll respond.

The future of AI

In all these years, we’ve just scratched the surface of what AI could do. Bots in the future will be able to do a lot more than today; they’ll wake you up in the morning, be your personal assistant or tutor, we’ll command them to wash the dishes and mowing. We would also instruct them to do other household chores like cooking and cleaning. But, this is doesn’t stop here; robots will be smart enough to know for themselves when the grass is getting too long, and when the dishes are overloaded and ready to wash, they’ll also know that which dishes need a hard scrubbing while china needs a gentle touch. Bots will become highly proactive, helping us in ways we can barely imagine today.

Where are you in all this?

All this is bound to happen, but the big question is, where are you? Are you on the bandwagon? Or at least aware of what’s happening and is likely to happen? Or are you already investing in it somehow? If there is any way, you could kick start an AI company that would make smart home appliances, a new voice bot, robot development platform or a checking company that offers to test out different AI bots from other companies then do it, and do it now!

The market is in need of such developments, and if you are planning to start one, then let us know as we’ll happy to help you in any and every way possible.

Artificial Intelligence: Impacting Our Lives

The growth potential of artificial intelligence is limitless, as it possesses the capacity to converge, transform and innovate lives around the world. Being a seamless blend of human intelligence and machine learning, AI has permeated itself into our daily lives, be in through our phones, in our homes or through the entrepreneurial ventures we run. Implemented with appropriate strategic planning, AI holds power to push not just small-businesses, but tech-giants to the forefront.

Through harnessing this ingenious technology, businesses can make their processes more efficient and more effective, resulting in a tailored experience for their consumers. With it taking the entrepreneurial realm by storm, AI has tactfully found its way into our day-to-day necessities, as an increasingly escalating proportion of the global population relies on the assistance it provides. Be it through our social media feed, the availability of autonomous cars, the usage of music streaming services, or be it the video games we play, AI has engulfed us into its cocoon.

Artificial Intelligence in the Entrepreneurial Ambit

Due to it being a relativity new form of technology, most individuals are unaware of its potential. In essence, it is a disruptive technology, which propels businesses and entrepreneurs alike, to experiment and step out of their comfort zone. Being unaware of how to implement the technology, most companies shy away from harnessing its usage, while, at a sharp contrast, an artificial intelligence driven company paces ahead with the experiences it offers, the products it creates and the services it tailors.

The technology demands companies to not just adopt it, but necessitates an entrepreneur to bring a shift in their culture, as the very core of the technology empowers businesses to push boundaries. With technology becoming affordable, it becomes more accessible to small-sized businesses, leading them to translate their tech ideas into a perceivable reality. Artificial intelligence aims to provide better solutions, as it is a malleable technology, learning and shaping itself the way it is required, leading the human race to gain insights that are otherwise impossible to tap into.

Artificial Intelligence in Our Personal Spheres

With the technology making inroads and creating breakthroughs in the automobile, architectural, health and educational sector, it only makes sense for it to trickle into our personal spheres. In the 21st century, major tech companies aren’t just contributing through researching, but they and venture capitalists are funding start-ups with capital, to make an impact and push forward ideas that disrupt the industry. This funding has trickled into our lives, as through the advent of the technology, our lives have become faster, easier and more automated, impregnating a culture of tech-dependency within us.

While the technology may seem intrusive, it can prove to be helpful for individuals with physical disabilities, for expediting important processes and for researching industries that engage in life-saving procedures, e.g. medical sector.

By analysing our actions, AI can predict our future in infinite possibilities, leading us to receive curated media playlists, personalised responses and customised shopping lists. Due to its degree of saturation, we communicate with our family members and colleagues using the technology, we use facial recognition to unlock our phones or use our virtual personal assistants, e.g. Alexa or Siri to go about our daily routine simply. While at a surface level, the intention of an AI company might seem invasive and threatening. Yet, AI leads the human race to a future where harnessing the intelligence of the human mind, in collaboration with technology, we drive conversations, drive experiences and drive lives to their utmost potential.

Nurturing the Future with Artificial Intelligence

By pairing together the right access, the right talent pool and the right ideas, AI has brought a paradigm shift to our lives. Fuelling this progression and expediting the speed, precision and effectiveness of processes, AI Seed, invests in start-up businesses that spearhead the initiative of developing machine learning and artificial intelligence. This pushes start-ups to rise to the challenge and put their innovative skills to test.

Artificial intelligence has attained itself this spotlight, as rather than completely taking over, it simply improves the capabilities and features of the applications, appliances or products we presently utilise. This amplifies its value, as it enriches an experience, with its strand of ingeniousness and sheer cognisance of the human mind.

The 21st Century: Integrating Marketing with Artificial Intelligence

Looking at AI through the lens of an entrepreneurial objective, rather than a technology provides us with a clear perspective. In this day and age of excessive consumption, where consumer trends are consistently shifting, it is of paramount importance for businesses to pace ahead or either stay on top of things, as otherwise, they run of risk of losing their market dominance and their consumer base.

By indulging in an AI investment, these businesses can create a breakthrough. Merely assimilating their marketing campaigns with artificial intelligence, can empower them to tap into insights otherwise impossible to reach. With the need and endeavour of enhancing user experience, businesses can delve into the intricate strands of the technology, leading them to yield favourable results.

A Widening Pool of Artificial Intelligence Ideas

This access to ingenious artificial intelligence ideas is, however, possible due to revolutionary ideas. These ideas, springing into reality, are initiated by start-ups having secured artificial intelligence investment. Due to the sole intention of creating immersive experiences, businesses can adopt various components of AI for planning, activating and measuring their marketing campaigns.

  • Harnessing The Use Of Artificial Emotional Intelligence (AEI)

The advent of this technology promises promising results, as the business wields power to personalise the experience at every stage. The concept AEI, harnesses a varying range of emotions, behaviours and emotional data, to enable a business to polish their online marketing initiatives. With human emotions being at the very epicentre of prompting a purchasing decision, AEI empowers brands to capitalise upon this data. Using a consumer’s mood to drive sales, has become a reality in the 21st century, as through using smart devices, computers, and biosensors, marketers can accordingly shape their campaigns.

Leveraging real-time empathetic marketing, marketers start possessing the capacity to speed the conversion rate, as they gain an inroad into addressing the consumer with what resonates with them. Rather than spending on short-lived trends, AI empowers businesses to invest in behaviour, which while isn’t sustainable, however, is easily trackable.

  • Refined Content Strategy

When it comes to marketing, increased ROI, increased campaign performance and increased consumer data insight take centre stage. By understanding the behaviour and buying patterns of consumers on an individual basis, marketers can set the relevant steps into place for delivering the ideal content to the ideal consumer, pushing them to engage in a purchasing action. Through the use of big data, marketers can begin to tap into and analyse data that can impact each segment of their production process.

  • Consumer Retention

In the era of social media platforms and instantaneous results, consumers are habituated with instant results. Bridging the gap between the brand and the consumer becomes easy for a marketer, as placing bots, to respond to questions and solve queries, can empower a brand to build a dynamic that is relatively costly when using human labour.

  • Advertisements

By adopting AEI, marketers can influence their businesses to bring a shift in the value, essence and storytelling behind the advertisements they produce. Aligning themselves with the interest of the consumer, they can tweak elements, resulting in the advertisement connecting and resonating with a wider range of current and potential consumers. With the ability of AEI to impact product development to creative testing, businesses need to rise and align themselves with the technology of the current times.

  • Elimination Of Time-Consuming & Imprecise Evaluations

Due to the presence of human effort, imprecise results or gaps are an inevitability. At a sharp contrast, AEI provides marketers with an insight that is backed by analysation of huge volumes of data, resulting in insights that are precise, faster to obtain and applicable in real-time. By capitalising upon advanced machine learning algorithms, marketers can optimise their layout, their copywriting, and targeting, creating an experience where each element is fine-tuned according to the consumer’s behavioural pattern.

Marketers consistently work toward building customer trust and understanding consumer behaviour. That said, it is important to understand how AEI only showcases its efficacy if complemented with the adequate interpretation of the data. For building a culture of artificial intelligence and for promoting the usage of AI to push forward the human race, several initiatives are rising to the forefront, such as an AI SEED company financing tech start-ups in their nascent stages, to create clutter-breaking ideas.