Where robotics and artificial intelligence meet

Even beyond the fully autonomous robots that appeared in the second half of the 20th Century, human beings have long been fascinated by automata. From Da Vinci’s mechanical knight  (designed around 1495) to Vaucanson’s “digesting duck” of 1739, and John Dee’s flying beetle of 1543 (which caused him to be charged with sorcery), the history of robotics stretches well before 1941 when Isaac Asimov first coined the term in one of his short stories.

Robotics combines computer science and mechanical engineering to construct robots that are able to assist humans in various tasks. We’re used to seeing industrial robots in manufacturing, for example, in the construction of cars. And, robots have been, and continue to be, particularly useful in heavy industry to help with processes that could be dangerous for humans resulting in injury or even death. Robotic arms, sometimes known as manipulators, were originally utilised in the handling of radioactive or biohazardous materials which would cause damage to human tissues and organs with exposure.

ABB Robotics is one of the leading multinational companies that deals in service robotics for manufacturing. Robotics applications include welding, heavy lifting, assembly, painting and coating, bonding and sealing, drilling, polishing, and palletising and packaging. These are all heavy-duty tasks that require a variety of end effectors, but robotics technology has progressed considerably and can also be seen in healthcare carrying out sophisticated medical procedures.

With the global pandemic, advances in robotics for surgery have been invaluable, allowing surgeons to remotely control the procedure from a safe distance. Now this technology is being developed further to allow surgeons to log in to operating theatres anywhere in the world, using remote control systems on a tablet or a laptop. They can then carry out surgery with the assistance of medical robotics on site. The technology was created by Nadine Hachach-Haram, who grew up in war-torn Lebanon, and there is no doubt it will be put to good use in locations where there is medical inequality or conflict or both.    

Mobile robots are also fairly common in various industries but we have yet to see them take over domestic chores in a way that perhaps the inventors of Roomba (the autonomous robotic vacuum cleaner) may have hoped they would. And yet, research has shown that people can tell what kind of personality a Neato Botvac has simply by the way that it moves. The study has provided interesting insights into human-robot interaction. Another study in 2017 demonstrated that anthropomorphic robots actually made people feel less lonely. People who work alongside collaborative robots, whether in the military or in manufacturing, also tend to express affection for their “cobots”.

Building on this affection that it seems people can and do develop for robots, Amazon has created Astro, a rolling droid that incorporates AI and is powered by Alexa, Amazon’s voice assistant. Astro also offers a detachable cup holder if your hands are too full to carry your coffee into the next room. Amazon has revolutionised automation with the use of Kiva robots in its warehouses and data science in its online commerce. So, it will be interesting to see if it can succeed where others have failed in making home robots popular.

What’s the difference between artificial intelligence and robotics?

While artificial intelligence refers only to the computer programs that process immense amounts of information in order to “think”, robotics refers to a machine designed to carry out assistive tasks that don’t necessarily require intelligence. Artificial intelligence has given us neural networks built on machine learning, which mimic the neural pathways of the human brain. It seems like an obvious next step would be to integrate these powerful developments with robotic systems to create intelligent robots.

Tesla’s self-driving cars contain neural networks that use autonomy algorithms to support the car’s real-time object detection, motion planning, and decision-making in complicated real-world situations. This level of advanced architectures and programming in the use of robotics is now being proposed with the Tesla Bot, a humanoid robot that Elon Musk says is designed to eliminate dangerous, repetitive, and boring tasks. Andrew Maynard, Associate Dean at the College of Global Futures, Arizona State University voices caution with regard to “a future that, judging by Musk’s various endeavours, will be built on a set of underlying interconnected technologies that include sensors, actuators, energy and data infrastructures, systems integration and substantial advances in computer power.” He adds that “before technology can become superhuman, it first needs to be human – or at least be designed to thrive in a human-designed world.”

It’s an interesting perspective, and one that goes beyond the usual moral and ethical concerns of whether robots could be used for ill, a theme often explored in science fiction films like The Terminator, Chappie, and Robot and Frank. Science fiction has always provided inspiration for actual technologies but it also serves to reflect back some of our own moral conundrums. If, as Elon Musk has indicated, the Tesla Bot “has the potential to be a generalised substitute for human labour over time,” what does this mean for artificially intelligent robotics? Would we essentially be enslaving robots? And, of course, this is built on the belief that the foundation of the economy will continue to be labour.

Tesla Bot hasn’t yet reached prototype stage yet, so whether Musk’s vision becomes a reality remains to be seen. The kinematics required to support bi-ped humanoid robots, though, are extremely complex. When it comes to bipedal robot design, LEONARDO (LEgs ONboARD drOne) is a quadcopter with legs. Only 76cm tall with an extremely light structure, even with an exoskeleton, LEONARDO looks far from the humanoids that the robotics industry may believe will become embedded in our everyday lives. Yet his multimodal locomotion system solves (or perhaps simply avoids) some of the issues real-world bipedal robots experience related to weight and centre of gravity. Mechatronics is an interdisciplinary branch of engineering that concerns itself with these kinds of issues. Some would say that mechatronics is where control systems, computers, electronic systems, and mechanical systems overlap. However, others would say that it’s simply a buzzword which is interchangeable with automation, robotics, and electromechanical engineering.

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Artificial intelligence is vital to take robotics research to the next level and enable robots to go beyond the relatively simple tasks they can complete on their own, as well as the more complex tasks they support humans with. Research in areas such as machine learning, distributed artificial intelligence, computer vision, and human-machine interaction will all be key to the future of robotics. 

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The use of statistics in data science

Statistics is the study of data. It’s considered a mathematical science and it involves the collecting, organising, and analysing of data with the intent of deriving meaning, which can then be actioned. Our everyday usage of the internet and apps across our phones, laptops, and fitness trackers has created an explosion of information that can be grouped into data sets and offer insights through statistical analysis. Add to this, 5.6 billion searches a day on Google alone and this means big data analytics is big business.

Although we may hear the phrase data analytics more than we hear reference to statistics nowadays, for data scientists, data analysis is underpinned by knowledge of statistical methods. Machine learning takes out a lot of the statistical methodology that statisticians would usually use. However, a foundational understanding of some basics in statistics supports strategy in exercises like hypothesis testing. Statistics contribute to technologies like data mining, speech recognition, vision and image analysis, data compression, artificial intelligence, and network and traffic modelling.

When analysing data, probability is one of the most used statistical testing criteria. Being able to predict the likelihood of something happening is important in numerous scenarios, from understanding how a self-driving car should react in a collision to recognising the signs of an upcoming stock market crash. A common use of probability in predictive modelling is forecasting the weather, a practice which has been refined since it first arose in the 19th century. For data-driven companies like Spotify or Netflix, probability can help predict what kind of music you might like to listen to or what film you might enjoy watching next.

Aside from our preferences in entertainment, research has recently been focused on the ability to predict seemingly unpredictable events such as a pandemic, an earthquake, or an asteroid strike. Because of their rarity, these events have historically been difficult to study through the lens of statistical inference – the sample size can be so small that the variance is pushed towards infinity. However, “black swan theory” could help us navigate unstable conditions in sectors like finance, insurance, healthcare, or agriculture, by knowing when a rare but high-impact event is likely to occur. 

The black swan theory was developed by Nassim Nicholas Taleb, who is a critic of the widespread use of the normal distribution model in financial engineering. In finance, the coefficient of variation is often used in investment to assess volatility and risk, which may appeal more to someone looking for a black swan. In computer science though, normal distributions, standard variation, and z-scores can all be useful to derive meaning and support predictions.

Some computer science-based methods that overlap with elements of statistical principles include:

  • Time series, ARMA (auto-regressive) processes, correlograms
  • Survival models
  • Markov processes
  • Spatial and cluster processes
  • Bayesian statistics
  • Some statistical distributions
  • Goodness-of-fit techniques
  • Experimental design
  • Analysis of variance (ANOVA)
  • A/B and multivariate testing
  • Random variables
  • Simulation using Markov Chain Monte-Carlo methods
  • Imputation techniques
  • Cross validation
  • Rank statistics, percentiles, outliers detection
  • Sampling
  • Statistical significance

While statisticians tend to incorporate theory from the outset into solving problems of uncertainty, computer scientists tend to focus on the acquisition of data to solve real-world problems. 

As an example, descriptive statistics aims to quantitatively describe or summarise a sample rather than use the data to learn about the population that the data sample represents. A computer scientist may perhaps find this approach to be reductive, but, at the same time, could learn from the clearer consideration of objectives. Equally, a statistician’s experience of working on regression and classification could potentially inform the creation of neural networks. Both statisticians and computer scientists can benefit from working together in order to get the most out of their complementary skills.

In creating data visualisations, statistical modelling, such as regression models, is often used. Regression analysis is typically used in determining the strength of predictors, trend forecasting, and forecasting an effect, which can be represented in graphs. Simple linear regression relates two variables (X and Y) with a straight line. Nonlinear regression relates to two variables in a nonlinear relationship, represented by a curve. In data analysis, scatter plots are often used to show various forms of regression. Matplotlib allows you to build scatter plots using Python; Plotly will allow the construction of an interactive version.

Traditionally, statistical analysis has been key in helping us understand demographics through a census – a survey through which citizens of a country offer up information about themselves and their households. From the United Kingdom, where we have the Office for National Statistics to New Zealand, where the equivalent public service department is called StatsNZ, these official statistics allow governments to calculate data such as gross domestic product (GDP). In contrast, Bhutan famously measures Gross National Happiness (GNH). 

This mass data collection, mandatory upon every household in the UK, which goes back to the Domesday Book in England, could be said to hold the origins of statistics as a scientific field. But it wasn’t until the early 19th century that the census was really used statistically to offer insights into populations, economies, and moral actions. It’s why statisticians still refer to an aggregate of objects, events or observations as the population and use formulae like the population mean, which doesn’t have to refer to a dataset that represents citizens of a country.

Coronavirus has been consistently monitored through statistics since the pandemic began in early 2020. The chi-square test is a statistical method often used in understanding disease  because it allows the comparison of two variables in a contingency table to see if they are related. This can show which existing health issues could cause a more life-threatening case of Covid-19, for example. 

Observational studies have also been used to understand the effectiveness of vaccines six months after a second dose. These studies have shown that effectiveness wanes. Even more ground-breaking initiatives are seeking to use the technology that most of us hold in our hands every day to support data analysis. The project EAR asks members of the public to use their mobile phones to record the sound of their coughs, breathing, and voices for analysis. Listening to the breath and coughs to catch an indication of illness is not new – it’s what doctors have practised with stethoscopes for decades. What is new is the use of machine learning and artificial intelligence to pick up on what the human ear might miss. There are currently not enough large data sets of the sort needed to train machine learning algorithms for this project. However, as the number of audio files increases, there will hopefully be valuable data and statistical information to share with the world. 

A career that’s more than just a statistic

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What is reinforcement learning?

Reinforcement learning (RL) is a subset of machine learning that allows an AI-driven system (sometimes referred to as an agent) to learn through trial and error using feedback from its actions. This feedback is either negative or positive, signalled as punishment or reward with, of course, the aim of maximising the reward function. RL learns from its mistakes and offers artificial intelligence that mimics natural intelligence as closely as it is currently possible.

In terms of learning methods, RL is similar to supervised learning only in that it uses mapping between input and output, but that is the only thing they have in common. Whereas in supervised learning, the feedback contains the correct set of actions for the agent to follow. In RL there is no such answer key. The agent decides what to do itself to perform the task correctly. Compared with unsupervised learning, RL has different goals. The goal of unsupervised learning is to find similarities or differences between data points. RL’s goal is to find the most suitable action model to maximise total cumulative reward for the RL agent. With no training dataset, the RL problem is solved by the agent’s own actions with input from the environment.

RL methods like Monte Carlo, state–action–reward–state–action (SARSA), and Q-learning offer a more dynamic approach than traditional machine learning, and so are breaking new ground in the field.

There are three types of RL implementations: 

  • Policy-based RL uses a policy or deterministic strategy that maximises cumulative reward
  • Value-based RL tries to maximise an arbitrary value function
  • Model-based RL creates a virtual model for a certain environment and the agent learns to perform within those constraints

How does RL work?

Describing fully how reinforcement learning works in one article is no easy task. To get a good grounding in the subject, the book Reinforcement Learning: An Introduction by Andrew Barto and Richard S. Sutton is a good resource.

The best way to understand reinforcement learning is through video games, which follow a reward and punishment mechanism. Because of this, classic Atari games have been used as a test bed for reinforcement learning algorithms. In a game, you play a character who is the agent that exists within a particular environment. The scenarios they encounter are analogous to a state. Your character or agent reacts by performing an action, which takes them from one state to a new state. After this transition, they may receive a reward or punishment. The policy is the strategy which dictates the actions the agent takes as a function of the agent’s state as well as the environment.

To build an optimal policy, the RL agent is faced with the dilemma of whether to explore new states at the same time as maximising its reward. This is known as Exploration versus Exploitation trade-off. The aim is not to look for immediate reward, but to optimise for maximum cumulative reward over the length of training. Time is also important – the reward agent doesn’t just rely on the current state, but on the entire history of states. Policy iteration is an algorithm that helps find the optimal policy for given states and actions.

The environment in a reinforcement learning algorithm is commonly expressed as a Markov decision process (MDP), and almost all RL problems are formalised using MDPs. SARSA is an algorithm for learning a Markov decision. It’s a slight variation of the popular Q-learning algorithm. SARSA and Q-learning are the two most typically used RL algorithms.

Some other frequently used methods include Actor-Critic, which is a Temporal Difference version of Policy Gradient methods. It’s similar to an algorithm called REINFORCE with baseline. The Bellman equation is one of the central elements of many reinforcement learning algorithms. It usually refers to the dynamic programming equation associated with discrete-time optimisation problems.

The Asynchrous Advantage Actor Critic (A3C) algorithm is one of the newest developed in the field of deep reinforcement learning algorithms. Unlike other popular deep RL algorithms like Deep Q-Learning (DQN) which uses a single agent and a single environment, A3C uses multiple agents with their own network parameters and a copy of the environment. The agents interact with their environments asynchronously, learning with every interaction, contributing to the total knowledge of a global network. The global network also allows agents to have more diversified training data. This mimics the real-life environment in which humans gain knowledge from the experiences of others, allowing the entire global network to benefit.

Does RL need data?

In RL, the data is accumulated from machine learning systems that use a trial-and-error method. Data is not part of the input that you would find in supervised or unsupervised machine learning.

Temporal difference (TD) learning is a class of model-free RL methods that learn via bootstrapping from a current estimate of the value function. The name “temporal difference” comes from the fact that it uses changes – or differences – in predictions over successive time steps to push the learning process forward. At any given time step, the prediction is updated, bringing it closer to the prediction of the same quantity at the next time step. Often used to predict the total amount of future reward, TD learning is a combination of Monte Carlo ideas and Dynamic Programming. However, whereas learning takes place at the end of any Monte Carlo method, learning takes place after each interaction in TD.

TD Gammon is a computer backgammon program that was developed in 1992 by Gerald Tesauro at IBM’s Thomas J. Watson Research Center. It used RL and, specifically, a non-linear form of the TD algorithm to train computers to play backgammon to the level of grandmasters. It was an instrumental step in teaching machines how to play complex games.

Monte Carlo methods represent a broad class of algorithms that rely on repeated random sampling in order to gain numerical results that point to probability. Monte Carlo methods can be used to calculate the probability of:

  • an opponent’s move in a game like chess
  • a weather event occurring in the future
  • the chances of a car crash under specific conditions

Named after the casino in the city of the same name in Monaco, Monte Carlo methods first arose within the field of particle physics and contributed to the development of the first computers. Monte Carlo simulations allow people to account for risk in quantitative analysis and decision making. It’s a technique used in a wide variety of fields including finance, project management, manufacturing, engineering, research and development, insurance, transportation, and the environment.

In machine learning or robotics, Monte Carlo methods provide a basis for estimating the likelihood of outcomes in artificial intelligence problems using simulation. The bootstrap method is built upon Monte Carlo methods, and is a resampling technique for estimating a quantity, such as the accuracy of a model on a limited dataset.

Applications of RL

RL is the method used by DeepMind to initiate artificial intelligence in how to play complex games like chess, Go, and shogi (Japanese chess). It was used in the building of AlphaGo, the first computer program to beat a professional human Go player. From this grew the deep neural network agent AlphaZero, which taught itself to play chess well enough to beat the chess engine Stockfish in just four hours.

AlphaZero has only two parts: a neural network, and an algorithm called Monte Carlo Tree Search. Compare this with the brute force computing power of Deep Blue, which, even in 1997 when it beat world chess champion Garry Kasparov, allowed the consideration of 200 million possible chess positions per second. The representations of deep neural networks like those used by AlphaZero, however, are opaque, so our understanding of their decisions is restricted. The paper Acquisition of Chess Knowledge in AlphaZero explores this conundrum.

Deep RL is being proposed in the use of unmanned spacecraft to navigate new environments, whether it’s Mars or the Moon. MarsExplorer is an OpenAI Gym compatible environment that has been developed by a group of Greek scientists. There are four deep reinforcement learning algorithms that the team has trained on the MarsExplorer environment, A3C, Ranbow, PPO, and SAC, with PPO performing best. MarsExplorer is the first open-AI compatible reinforcement learning framework that is optimised for the exploration of unknown terrain.

Reinforcement learning is also used in self-driving cars, in trading and finance to predict stock prices, and in healthcare for diagnosing rare diseases.

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Corporate culture: the building blocks of a business

It’s not always easy to put a finger on what makes a workplace feel the way it does. Is it rooted in the work that takes place there? The other employees? The work environment itself?

In fact, it’s all of this – and more. The unique culture of a business is the golden thread that runs through every aspect of its operations.

Increasingly, people are seeking to address their work-life balance – acutely evidenced by what has been dubbed “The Great Resignation” witnessed throughout the pandemic. An organisation’s culture is often at the heart of decisions to leave or join an employer. With many now viewing work as more than a paycheck – in a world where going solo is seen as less of a risk – businesses can’t afford to ignore substandard cultures. To retain talented individuals, they need to create environments in which people can thrive.

What is corporate culture?

Corporate culture describes and governs the ways in which a business operates. It refers to its personality and character: shared values, beliefs and assumptions about how people should act; how decisions should be made; and how work activities should be carried out. Culture denotes the particular ideas and customs that make each organisation unique. For example, its leadership, job roles, company values, workspace, pay, initiatives and perks, rewards and recognition. Cumulatively, it should be the foundation upon which people can work to the best of their ability.

The core elements that make up a company’s culture include:

  • Leadership
  • Vision and values
  • Recognition
  • Operations
  • Learning and development
  • Environment
  • Communication
  • Pay and benefits
  • Wellbeing

From small start-ups to established, global brands, all businesses have a workplace culture – and they vary dramatically. The various types include conventional, clan, progressive, market, adhocracy, authority organisation, and more.

Take the retail brand Zappos, a company where creating an inclusive culture is the top priority. Their core value lies in celebrating every employee’s diversity and individuality – and they’re famous for it. However, this approach is starkly different to that taken by countless other businesses.

Why is corporate culture important?

Experts in the company culture space, Liberty Mind, make a compelling case for why improving corporate culture should be prioritised:

  • 88% of employees believe a distinct workplace culture is important to company success
  • Companies with strong cultures saw 4x increase in revenue growth
  • 58% of people say that they trust strangers more than their own boss
  • 78% of executives included culture among the top 5 things that add value to their company
  • Job turnover in organisations with positive cultures is 13.9%, whereas in organisations with poor cultures it’s 48.4%
  • Only 54% of employees recommend their company as a good place to work
  • More than 87% of the global workforce is not engaged, yet engaged workplaces are 21% more profitable

However, it seems many organisations are struggling to get it right; 87% of organisations cite culture and employee engagement as one of their top challenges.

Strengths and weaknesses of culture

Clearly, culture matters to employees – and therefore has a direct impact on a business. Workplaces with poor or non-existent cultures are more likely to encounter low morale, brand reputation issues and decreased productivity. As people leave their roles, poor employee retention necessitates costs associated with recruitment, training and – at least in the short-term – an increased workload for already-beleaguered employees. Worse still, workplaces with toxic cultures breed resentment, fear, frustration and poor mental health among their employees. If staff do not simply leave, as many will, they are likely to take more sick days and be less productive.

In contrast, companies with nurturing, strong cultures can expect to reap the rewards. They are likely to feature good teamwork: teams working towards shared goals are more driven and productive, with the ability to resolve issues more quickly. Brand reputation will soar as employees – who have belief in company leaders and their shared values – spread the word, acting as brand ambassadors. These businesses are in a better position to weather change, attract and retain high quality applicants, and to take risks and make decisions. Together, these positive, culture-building aspects are likely to improve a company’s bottom line.

Improving company culture: more than a mission statement

By first assessing the current cultural status, a company is in a stronger position to identify – and design a roadmap to achieve – its desired culture. With input from stakeholders, leaders should examine the current culture, including core values, strengths, and organisational impact. Harvard Business Review designed a tool to understand an organisation’s cultural profile, supporting this investigative work. It guides leaders to examine cultural styles and types of cultures, the prominence of company culture, and demographic aspects of how culture operates.

Next, leaders must understand how strategy and business environment impact the culture. Are there any current or future external conditions or strategic decisions that will influence cultural styles? If so, how can the styles respond? Any robust culture target will need to support, or respond to, future changes.

It’s critical to ground the target in business realities. Leaders should frame any culture targets in response to real-world problems and value-adding solutions – far more practical and effective than selling them as shiny, culture change initiatives.

Leaders must be prepared to drive cultural change through every area of the business. Indeed suggest further actions to support a company-wide cultural improvement:

  • Hire the right people
  • Appoint a cultural ambassador
  • Set specific, achievable goals with clear metrics
  • Encourage open communication
  • Reward success and offer incentives
  • Organise meaningful team-building and social events

Boosting employee engagement through company culture is not about making people happy. Instead, leaders should focus on making them feel connected to the business and motivated to help achieve its goals, even during times of adversity.

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What is data science?

Data science includes the fields of statistics, scientific methods, artificial intelligence (AI) and data analysis. Every day, huge amounts of data are collected from our usage of the internet, our phones, the Internet of Things, and other objects embedded with sensors that provide data sources. This mass of information can be used by data scientists to create algorithms for data mining and data analysis from machine learning.

Once machines are conversant in what they’re looking for, they can potentially create their own algorithms from looking at raw data. This is called deep learning, which is a subset of machine learning. It usually requires some initial supervised learning techniques, for example, allowing the machine to scan through labelled datasets created by data scientists. However, because the machine will be powered by a neural network of at least three layers, its thinking simulates that of the human brain, and it can start noticing patterns beyond its specific training.

What’s the difference between data science and computer science?

Computer science and data science are sometimes used interchangeably. Pure computer science is focused on software, hardware and offering advances in the capacity of what computers can do with data collection. Data science is more interdisciplinary in scope, and involves aspects of computer science, mathematics, statistics and knowledge of specific fields of enquiry. Computer scientists use programming languages like Java, Python, and Scala. Data analysts are likely to have basic knowledge of SQL – the standard language for communicating with databases – as well as potentially R or Python for statistical programming. 

Data analytics is concerned with telling a compelling story based on data that’s been sorted by machine learning algorithms. Although data analysts are expected to have some programming skills, their role is more concerned with interpreting and presenting clear and easily understandable data visualisations. This could be data that supports an argument, or data that proves an assumption wrong.

Data engineers are part of the data analytics team, but they work further up the pipeline (or lifecycle as it’s sometimes known) overseeing and monitoring data retrieval, as well as storage and distribution. Hadoop is the most used framework for storing and processing big data. The Hadoop distributed file system (HDFS) means that data can be split and saved across clusters of servers. This is economical and easily scalable as data grows. The MapReduce functional programming model adds speed to the equation. MapReduce performs parallel processing across datasets rather than sequential processing, which significantly speeds things up.

Why data science is important

Data science is most commonly used for predictive analysis. This helps with forecasting and decision-making in a wide spectrum of areas from weather to cybersecurity, risk assessment and FinTech. Statistical analysis helps businesses make decisions with confidence in an increasingly unpredictable world. It also offers up insight into broader trends or helps zero-in on a particular consumer segment, which can give businesses a competitive advantage. Big names like McKinsey and Nielsen use data to report on larger sector-wide trends and provide analysis on the effects of geopolitical and socio-economic events. Many organisations pay good money for these reports so that they can plan and stay ahead of the curve. 

In the 21st century, AI and big data are revolutionising whole industries such as insurance, financial services and logistics. In healthcare, big data enables faster identification of high-risk patients, more effective interventions, and closer monitoring. Public transport networks can function more economically and sustainably thanks to data analysis. As the climate crisis increases the frequency of extreme weather, improved forecasting can help to mitigate the worst of the damage.

Data science is the fastest growing job area on LinkedIn and is predicted to create 11.5 million jobs by 2026 according to the US Bureau of Labour Statistics. Many leading tech-based companies like LinkedIn, Facebook, Netflix and Deliveroo rely heavily on data science and are driving demand for analysts. 

How to learn data science

Data science tutorials can be found all over the internet and you can get a reasonable understanding of how it works from these, as well as certification – for example, from Microsoft on Azure. However, for professionals, a qualification like an MSc Data Science or a postgraduate degree in an associated subject area like an MSc Computer Science with Data Analytics is highly valued by employers. This can be studied full-time or part-time while you gain work experience in the area you wish to specialise in. Academia can only take you so far in understanding the theory but working hands-on in the world of data science will help you in the practice of this subject, honing your skills. It’s not one of the prerequisites for taking on a role, but it will help you stand out from the crowd in a competitive job market.

Data science is a burgeoning field that can complement most of the social sciences and there is an increasing demand for expertise in this area. Data scientists can come from a wide variety of backgrounds such as the fields of psychology, sociology, economics, and political science, because data and statistics are valuable and applicable to all these areas.

A score of 6.5 in IELTS (the International English Language Testing System) is one of the entry requirements for a degree in data science or computer science. This is because English is considered a first language in data science internationally, but also because natural language processing works off the English language as a primary reference point when programming in Python.

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The role of natural language processing in AI

What is natural language processing?

Natural language processing (NLP) is a branch of artificial intelligence within computer science that focuses on helping computers to understand the way that humans write and speak. This is a difficult task because it involves a lot of unstructured data. The style in which people talk and write (sometimes referred to as ‘tone of voice’) is unique to individuals, and constantly evolving to reflect popular usage.

Understanding context is also an issue – something that requires semantic analysis for machine learning to get a handle on it. Natural language understanding (NLU) is a sub-branch of NLP and deals with these nuances via machine reading comprehension rather than simply understanding literal meanings. The aim of NLP and NLU is to help computers understand human language well enough that they can converse in a natural way.

Real-world applications and use cases of NLP include:

  • Voice-controlled assistants like Siri and Alexa.
  • Natural language generation for question answering by customer service chatbots.
  • Streamlining the recruiting process on sites like LinkedIn by scanning through people’s listed skills and experience.
  • Tools like Grammarly which use NLP to help correct errors and make suggestions for simplifying complex writing.
  • Language models like autocomplete which are trained to predict the next words in a text, based on what has already been typed.

All these functions improve the more that we write, speak, and converse with computers: they are learning all the time. A good example of this iterative learning is a function like Google Translate which uses a system called Google Neural Machine Translation (GNMT). GNMT is a system that operates using a large artificial neural network to increase fluency and accuracy across languages. Rather than translating one piece of text at a time, GNMT attempts to translate whole sentences. Because it scours millions of examples, GNMT uses broader context to deduce the most relevant translation. It also finds commonality between many languages rather than creating its own universal interlingua. Unlike the original Google Translate which used the lengthy process of translating from the source language into English before translating into the target language, GNMT uses “zero-shot translate” – translating directly from source to target.

Google Translate may not be good enough yet for medical instructions, but NLP is widely used in healthcare. It is particularly useful in aggregating information from electronic health record systems, which is full of unstructured data. Not only is it unstructured, but because of the challenges of using sometimes clunky platforms, doctors’ case notes may be inconsistent and will naturally use lots of different keywords. NLP can help discover previously missed or improperly coded conditions.  

How does natural language processing work?

Natural language processing can be structured in many different ways using different machine learning methods according to what is being analysed. It could be something simple like frequency of use or sentiment attached, or something more complex. Whatever the use case, an algorithm will need to be formulated. The Natural Language Toolkit (NLTK) is a suite of libraries and programs that can be used for symbolic and statistical natural language processing in English, written in Python. It can help with all kinds of NLP tasks like tokenising (also known as word segmentation), part-of-speech tagging, creating text classification datasets, and much more.

These initial tasks in word level analysis are used for sorting, helping refine the problem and the coding that’s needed to solve it. Syntax analysis or parsing is the process that follows to draw out exact meaning based on the structure of the sentence using the rules of formal grammar. Semantic analysis would help the computer learn about less literal meanings that go beyond the standard lexicon. This is often linked to sentiment analysis.

Sentiment analysis is a way of measuring tone and intent in social media comments or reviews. It is often used on text data by businesses so that they can monitor their customers’ feelings towards them and better understand customer needs. In 2005 when blogging was really becoming part of the fabric of everyday life, a computer scientist called Jonathan Harris started tracking how people were saying they felt. The result was We Feel Fine, part infographic, part work of art, part data science. This kind of experiment was a precursor to how valuable deep learning and big data would become when used by search engines and large organisations to gauge public opinion.

Simple emotion detection systems use lexicons – lists of words and the emotions they convey from positive to negative. More advanced systems use complex machine learning algorithms for accuracy. This is because lexicons may class a word like “killing” as negative and so wouldn’t recognise the positive connotations from a phrase like, “you guys are killing it”. Word sense disambiguation (WSD) is used in computational linguistics to ascertain which sense of a word is being used in a sentence.

Other algorithms that help with understanding of words are lemmatisation and stemming. These are text normalisation techniques often used by search engines and chatbots. Stemming algorithms work by using the end or the beginning of a word (a stem of the word) to identify the common root form of the word. This technique is very fast but can lack accuracy. For example, the stem of “caring” would be “car” rather than the correct base form of “care”. Lemmatisation uses the context in which the word is being used and refers back to the base form according to the dictionary. So, a lemmatisation algorithm would understand that the word “better” has “good” as its lemma.

Summarisation is an NLP task that is often used in journalism and on the many newspaper sites that need to summarise news stories. Named entity recognition (NER) is also used on these sites to help with tagging and displaying related stories in a hierarchical order on the web page.

How does AI relate to natural language processing?

Natural language processing – understanding humans – is key to AI being able to justify its claim to intelligence. New deep learning models are constantly improving AI’s performance in Turing tests. Google’s Director of Engineering Ray Kurzweil predicts that AIs will “achieve human levels of intelligence” by 2029.

What humans say is sometimes very different to what humans do though, and understanding human nature is not so easy. More intelligent AIs raise the prospect of artificial consciousness, which has created a new field of philosophical and applied research.

Interested in specialising in NLP?

Whether your interest is in data science or artificial intelligence, the world of natural language processing offers solutions to real-world problems all the time. This fascinating and growing area of computer science has the potential to change the face of many industries and sectors and you could be at the forefront. 

Find out more about NLP with an MSc Computer Science with Artificial Intelligence from the University of York.

Workplace diversity and why it matters

Diversity in the workplace is at the forefront of creating healthier, more inclusive environments. The office or space in which we work should be leading on equality and providing a work environment where all people are welcome and feel comfortable, regardless of ethnicity, race, sexual orientation, age, sex, or gender. Diversity also includes diversity of thought, education, skill sets, experiences, beliefs, and personalities. 

Unfortunately, it is usually cases of discrimination which bring these issues into sharp focus and motivate organisations to improve their Diversity, Equity and Inclusion (DEI) Initiatives. The Black Lives Matter movement has catalysed many organisations to implement unconscious bias training. Structural, institutional change is required, without which diversity training may simply be a box-ticking exercise. Unconscious biases are just one aspect of how discrimination and prejudice can play out; strong policies, strategies and practices should be enforced company-wide to support any diversity training.

Alice Thompson, a working mother, recently won a landmark sex discrimination court case because her employer wouldn’t allow her to work part-time and leave work early to pick up her child from nursery. Working parents expect employers to be flexible and reasonable in consideration of family commitments. Historically, it has been women who must balance these commitments with their work, but no parent should feel discriminated against if they have family commitments or request parental leave. In male-dominated sectors which still lack gender diversity, women can feel particularly isolated when making these decisions, so it is imperative that HR teams are also diverse.

Why is workplace diversity important?

Job seekers are increasingly looking for roles within diverse companies that promote an inclusive culture. Whether or not a company truly has a diverse workforce can be key in the decision-making of potential candidates going through the recruitment process. This is particularly true for Millennials who expect employers to be progressive and proactive in this area. 

Prospective employees look to see a reflection of themselves in diverse teams. Organisations can draw skilled candidates to their talent pool and improve employee engagement by offering a genuinely diverse workplace. Talented candidates may have many job offers but money may not be the deciding factor for them. Seeing a team of people from different backgrounds with different perspectives can help them feel confident that an employer really does champion equal opportunity.

The benefits of diversity in the workplace also extend to profitability as demonstrated in a 2019 McKinsey analysis. Companies in the top quartile for gender diversity on executive teams were 25% more likely to have above-average profitability than companies in the fourth quartile. A more recent report from McKinsey, Diversity Wins, stated that companies in the top quartile for ethnic and racial diversity in management were 36% more likely to have financial returns above the industry mean. These statistics present a strong business case for diversity, showing that it is in the interests of both the employer and the employee.   

A sense of belonging was at the top of Deloitte’s 2020 Global Human Capital Trends survey as one of the most important human capital issues alongside well-being. 93% of survey respondents agreed that belonging drives organisational performance. This is one of the highest rates of consensus seen in a decade of Global Human Capital Trends reports, pointing to a societal shift that demands the attention of corporations. 

As we continue to live through a time of challenge and change, individuals must be seen for their skills and talents regardless of race, neurodiversity, physical abilities, sexuality, gender, or age. The ethnic diversity of young British people is also in stark contrast with the demographics of the past. Inclusivity for them is less of a concept and more of a natural way of doing and being.   

Encouraging diversity in the workplace

Just as unconscious bias training needs the support of strong policies, DEI initiatives must be well-considered, dynamic and responsive. Many organisations may communicate their DEI initiative through social media, but this can be perceived as performative and potential new employees will scrutinise the company culture and diversity goals for authenticity. For this reason, how workplace culture is communicated both externally and internally is of key importance.

Diversity is a continuous process so updates should be exactly that, updates on an ongoing path to diversity goals. The hiring process should introduce candidates to the company’s efforts in DEI and be transparent about whether it is an inclusive workplace in practice. A workplace can appear to be diverse, but inclusivity usually shows in how relaxed current employees feel in being their authentic selves.

Diversity also needs to be led from the top so hiring managers and leaders who are fully on-board with the DEI initiative and who are confident communicators is crucial to keeping momentum. How leaders behave and communicate trickles down through the organisation. This doesn’t mean that everyone has to agree on everything, it means cultivating an environment where people feel safe having open discussions and debates where their viewpoints are accepted even if they aren’t agreed with. Conversely, those who desire confidentiality or minority groups who do not feel safe speaking in an open-plan office, should be offered respect and discretion.

Diverse companies attract diverse talent

A strong diversity and inclusion initiative is becoming a given for leading corporations and organisations worldwide, especially if they want to attract talented and skilled individuals from all backgrounds. DEI is not something that can simply be mentioned in job descriptions to capture the attention of applicants, it is evident in how relaxed employees are in the workplace and how the workplace accommodates the needs of its employees.

Can you be an ambassador for diversity and inclusion in your organisation? Are you keen to learn more about what this means in an international business setting? Find out how you can with the University of York’s MSc International Business Leadership and Management online degree.

A guide to corporate social responsibility

Responsible business practices and a commitment to global social citizenship are needed to safeguard our shared future – and pave the way for a better world.

Some of the biggest issues facing our planet – including climate change, poverty, social inequality, food insecurity and human rights abuses – are ones that cannot be tackled without critical change within the world of business.

Businesses must play an integral part in shaping what happens next. From their environmental impact, to their work within local communities, to who’s involved in their decision making, any business model should be examined to identify where and how sustainability efforts could be supported.

What is corporate social responsibility?

Corporate social responsibility (CSR) is the idea that a business has a responsibility to the wider world. It’s a management concept whereby companies integrate social and environmental concerns in both their business operations and their interactions with stakeholders, offering a way for companies to achieve a balance of environmental, philanthropic, ethical and economic practices.

The triple bottom line (TBL) is the idea that businesses should prepare three distinct bottom-line measurements, also known as the three Ps: people, planet and profit. The TBL highlights the relationship between business and a ‘green mindset’; it attempts to align organisations with the goal of sustainable development, and positive impact, on a global scale. Ultimately, it offers a more rounded, comprehensive set of working objectives than simply profit-above-all.

CSR issues are wide ranging. They include environmental management, human rights, eco-efficiency, responsible sourcing and production, diversity and inclusion, labour standards and working conditions, social equity, stakeholder engagement, employee and community relations, governance, and anti-corruption policies.

Why is CSR important to businesses?

According to Impact, a leading social value measurement platform, CSR is good for business. They note that:

  • 77% of consumers are more likely to use companies that are committed to making the world a better place
  •  49% of consumers assume that companies who don’t speak on social issues don’t care
  • 25% of consumers and 22% of investors cite a “zero tolerance” policy toward companies that embrace questionable ethical practices
  • Consumers are four times as likely to purchase from a brand with a strong purpose
  • 66% of global consumers are willing to pay more for sustainable goods

On top of this, it’s estimated that CSR initiatives can help companies to avoid losses of roughly 7%. More and more businesses are publishing annual sustainability reports, in a bid for transparency in their efforts and operations and to benefit from its other advantages.

CSR is integral to the development of a more sustainable future. The better question for stakeholders wondering whether they can afford to spend time and energy implementing CSR strategies, is whether they can afford not to.

How can a business demonstrate CSR?

The United Nations Global Compact calls upon organisations to “align their strategies and operations with universal principles on human rights, labour, environment and anti-corruption, and take actions that advance societal goals”.

In addition to supporting businesses to aim for the prescribed United Nations Sustainable Development Goals, it asks them to adhere to ten Principles. The Principles outline measures across each of the key areas listed above. Examples of the measures include: the effective abolition of forced, compulsory and child labour; initiatives to promote greater environmental responsibility; the elimination of discrimination in respect of employment and occupation; and working against corruption in all forms, including extortion and bribery. It offers a framework and starting point for the minimum businesses must do in order to operate responsibly.

Similarly, in 2010, the International Organization for Standardization (ISO) launched new guidance: the ISO 26000. Designed for businesses who are committed to operating in a socially responsible way, it helps organisations to translate social principles into effective actions and shares best practice. Increasingly, a company’s adherence to ISO 26000 is regarded as a commitment to both sustainability and its overall performance.

Where CSR should be implemented in a business strategy depends on where improvement is required. If a business is energy-intensive, could that energy come from renewable sources? Where there’s a lack of diversity and inclusivity among employees, could human resource policies be revised? Could a multinational team of frequent flyers reduce their travel or offset their emissions?

The need for authenticity

Underscoring any CSR efforts is the need for authenticity.

In today’s world, the most respected brands don’t rely on virtue signalling – they live and breathe their values. A brand that is consistent in its actions is more likely to gain loyal followers and cultivate long-term corporate sustainability.

Modern consumers, particularly Millennials and Gen Z, are advocates for positive change. They demand more from brands and companies, increasingly wise to those whose claims ring false. One such example is prominent fast fashion brands who launch ‘sustainable’ or ‘recycled’ clothing lines while, behind the scenes, their predominantly female garment workers receive a less-than-living wage and suffer in deplorable working conditions. To use another example, many businesses also incorporate the rainbow flag in marketing efforts during Pride Month, while failing to support the LGBT+ community in any meaningful way.

Public relations activities fare better when brands are founded on an authentic, purposeful sustainability strategy.

The benefits of CSR

CSR programmes can be a powerful marketing tool. They can help a business to position itself favourably in the eyes of consumers, regulators and investors, boosting brand reputation. By commanding respect in the marketplace and gaining competitive advantage, CSR can result in better financial performance.

By default, business leaders who focus on improving their social impact will scrutinise business practices related to their value chain, consumer offerings, employee relations, and other operational aspects. This can result in new, innovative solutions which may also have cost-saving benefits. A business may reconfigure its manufacturing process to consume less energy and produce less waste; as well as being more environmentally friendly, it may also reduce its overheads.

CSR practices can boost employee engagement and satisfaction. Increasingly, people view their work as an extension of their own identities and convictions. When a brand invites them to share in its objectives, it can drive employee retention and attract quality candidates to roles.

Companies are embracing social responsibility due to moral convictions as well as profit – and reaping the benefits. All these effects of CSR can help to ensure that a company remains profitable and sustainable in the long term.

Champion CSR in your business sector

Are you passionate about environmental sustainability? Want to develop the skills and knowledge to pioneer global corporate citizenship? Interested in learning more about CSR activities?

The University of York’s online MSc International Business, Leadership and Management programme places particular emphasis on the challenges associated with global trade, marketing and sales, together with an overview of relevant management disciplines. You’ll be supported to build your knowledge of practice whilst developing an advanced theoretical understanding of the international business environment.

The next step in machine learning: deep learning

What is deep learning?

Deep learning is a sector of artificial intelligence (AI) concerned with creating computer structures that mimic the highly complex neural networks of the human brain. Because of this, it is also sometimes referred to as deep neural learning or deep neural networks (DNNs). 

A subset of machine learning, the artificial neural networks utilised in deep learning are capable of sorting much more information from large data sets to learn and consequently use in making decisions. These vast amounts of information that DNNs scour for patterns are sometimes referred to as big data.

Is deep learning machine learning?

The technology used in deep learning means that computers are closer to thinking for themselves without support or input from humans (and all the associated benefits and potential dangers of this). 

Traditional machine learning requires rules-based programming and a lot of raw data preprocessing by data scientists and analysts. This is prone to human bias and is limited by what we are able to observe and mentally compute ourselves before handing over the data to the machine. Supervised learning, unsupervised learning, and semi-supervised learning are all ways that computers become familiar with data and learn what to do with it. 

Artificial neural networks (sometimes called neural nets for short) use layer upon layer of neurons so that they can process a large amount of data quickly. As a result, they have the “brain power” to start noticing other patterns and create their own algorithms based on what they are “seeing”. This is unsupervised learning and leads to technological advances that would take humans a lot longer to achieve. Generative modelling is an example of unsupervised learning.

Real-world examples of deep learning

Deep learning applications are used (and built upon) every time you do a Google search. They are also used in more complicated scenarios like in self-driving cars and in cancer diagnosis. In these scenarios, the machine is almost always looking for irregularities. The decisions the machine makes are based on probability in order to predict the most likely outcome. Obviously, in the case of automated driving or medical testing, accuracy is more crucial, so computers are rigorously tested on training data and learning techniques.

Everyday examples of deep learning are augmented by computer vision for object recognition and natural language processing for things like voice activation. Speech recognition is a function that we are familiar with through use of voice-activated assistants like Siri or Alexa, but a machine’s ability to recognise natural language can help in surprising ways. Replika, also referred to as “My AI Friend”, is essentially a chatbot that gets to know a user through questioning. It uses a neural network to have an ongoing one-to-one conversation with the user to gather information. Over time, Replika begins to speak like the user, giving the impression of emotion and empathy. In April 2020, at the height of the pandemic, half a million people downloaded Replika, suggesting curiosity about AI but also a need for AI, even if it does simply mirror back human traits. This is not a new idea as in 1966, computer scientist Joseph Weizenbaum created what was a precursor to the chatbot with the program ELIZA, the computer therapist.

How does deep learning work?

Deep learning algorithms make use of very large datasets of labelled data such as images, text, audio, and video in order to build knowledge. In its computation of the content – scanning through and becoming familiar with it – the machine begins to recognise and know what to look for. Like the human brain, each computer neuron has a role in processing data, it provides an output by applying the algorithm to the input data provided. Hidden layers contain groups of neurons.

At the heart of machine learning algorithms is automated optimisation. The goal is to achieve the most accurate output so we need the speed of machines to efficiently assess all the information they have and to begin detecting patterns which we may have missed. This is also core to deep learning and how artificial neural networks are trained.

TensorFlow is an open source platform created by Google, written in Python. A symbolic maths library, it can be utilised for many tasks, but primarily for training, transfer learning, and developing deep neural networks with many layers. It’s particularly useful for reinforcement learning because it can calculate large numbers of gradients. The gradient is how the data is seen on a graph. So, for example, the gradient descent algorithm would be used to minimise error function and would be represented graphically as the gradient at its lowest possible point. The algorithm used to calculate the gradient of an error function is “backpropagation”, short for “backward propagation of errors”.

One of the most used deep learning models in reinforcement learning, particularly for image recognition, Convolutional Neural Networks (CNN) can learn increasingly abstract features by using deeper layers. CNNs can be accelerated by using Graphics Processing Units (GPUs) because they can process many pieces of data simultaneously. They can help perform feature extraction by analysing pixel colour and brightness or vectors in the case of grayscale.

Recurrent Neural Networks (RNNs) are considered state of the art because they are the first of their kind to use an algorithm that lets them remember their input. Because of this, RNNs are used in speech recognition and natural language processing in applications like Google Translate.

Can deep learning be used for regression?

Neural networks can be used for both classification and regression. However, regression models only really work well if they’re the right fit for the data and that can affect the network architecture. Classifiers in something like image recognition, have more of a compositional nature compared with the many variables that can make up a regression problem. Regression offers a lot more insight than simply, “Can we predict Y given X?”, because it explores the relationship between variables. Most regression models don’t fit the data perfectly, but neural networks are flexible enough to be able to pick the best type of regression. To add to this, hidden layers can always be added to improve prediction.

Knowing when to use regression or not to solve a problem may take some research. Luckily, there are lots of tutorials online to help, such as How to Fit Regression Data with CNN Model in Python.

Ready to discover more about deep learning?

The University of York’s online MSc Computer Science with Artificial Intelligence from the University of York is the ideal next step if your career ambitions lie in this exciting and fast-paced sector. 

Whether you already have knowledge of machine learning algorithms or want to immerse yourself in deep learning methods, this master’s degree will equip you with the knowledge you need to get ahead.

What is machine learning?

Machine learning is considered to be a branch of both artificial intelligence (AI) and computer science. It uses algorithms to replicate the way that humans learn but can also analyse vast amounts of data in a short amount of time. 

Machine learning algorithms are usually written to look for recurring themes (pattern recognition) and spot anomalies, which can help computers make predictions with more accuracy. This kind of predictive modelling can be for something as basic as a chatbot anticipating what your question may be about to something quite complex, like a self-driving car knowing when to make an emergency stop

It was an IBM employee, Arthur Samuel, who is credited with creating the phrase “machine learning” in his 1959 research paper, “Some studies in machine learning using the game of checkers”. It’s amazing to think that machine learning models were being studied as early as 1959 given that computers now contribute to society in important areas as diverse as healthcare and fraud detection.

Is machine learning AI?

Machine learning represents just a section of AI capabilities. There are three major areas of interest that use AI – machine learning, deep learning, and artificial neural networks. Deep learning is a field within machine learning, and neural networks is a field within deep learning. Traditionally, machine learning is very structured and requires more human intervention in order for the machine to start learning via supervised learning algorithms. Training data is chosen by data scientists to help the machine determine the features it needs to look for within labelled datasets. Validation datasets are then used to ensure an unbiased evaluation of a model fit on the training data set. Lastly, test data sets are used to finalise the model fit.

Unsupervised learning also needs training data, but the data points are unlabelled. The machine begins by looking at unstructured or unlabelled data and becomes familiar with what it is looking for (for example, cat faces). This then starts to inform the algorithm, and in turn helps sort through new data as it comes in. Once the machine begins this feedback loop to refine information, it can more accurately identify images (computer vision) and even carry out natural language processing. It’s this kind of deep learning that also gives us features like speech recognition. 

Currently, machines can tell whether what they’re listening to or reading was spoken or written by humans. The question is, could machines then write and speak in a way that is human? There have already been experiments to explore this, including a computer writing music as though it were Bach.

Semi-supervised learning is another learning technique that combines a small amount of labelled data within a large group of unlabelled data. This technique helps the machine to improve its learning accuracy.

As well as supervised and unsupervised learning (or a combination of the two), reinforcement learning is used to train a machine to make a sequence of decisions with many factors and variables involved, but no labelling. The machine learns by following a gaming model in which there are penalties for wrong decisions and rewards for correct decisions. This is the kind of learning carried out to provide the technology for self-driving cars.

Is clustering machine learning?

Clustering, also known as cluster analysis, is a form of unsupervised machine learning. This is when the machine is left to its own devices to discover what it perceives as natural grouping or clusters. Clustering is helpful in data analysis to learn more about the problem domain or understand arising patterns, for example, customer segmentation. In the past, segmentation was done manually and helped construct classification structures such as the phylogenetic tree, a tree diagram that shows how all species on earth are interconnected. From this example alone, we can see how what we now call big data could take years for humans to sort and compile. AI can manage this kind of data mining in a much quicker time frame and spot things that we may not, thereby helping us to understand the world around us. Real-world use cases include clustering DNA patterns in genetics studies, and finding anomalies in fraud detection.

Clusters can overlap, where data points belong to multiple clusters. This is called soft or fuzzy clustering. In other cases, the data points in clusters are exclusive – they can exist only in one cluster (also known as hard clustering). K-means clustering is an exclusive clustering method where data points are placed into various K groups. K is defined in the algorithm by the number of centroids (centre of a cluster) in a set, which it then uses to allocate each data point to the nearest cluster. The “means” in K-means refers to the average, which is worked out from the data in order to find the centroid. A larger K value is an indication of many, smaller groups, whereas a small K value shows larger, broader groups of data.

Other unsupervised machine learning methods include hierarchical clustering, probabilistic clustering (including the Gaussian Mixture Model), association rules, and dimensionality reduction.

Principal component analysis is an example of dimensionality reduction – reducing larger sets of variables in the input data without losing variance. It is also a useful method for the visualisation of high-dimensional data because it ranks principal components according to how much they contribute to patterns in the data. Although more data is generally helpful for more accurate results, it can lead to overfitting, which is when the machine starts picking up on noise or granular detail from its training data set.

The most common use of association rules is for recommendation engines on sites like Amazon, Netflix, LinkedIn, and Spotify to offer you products, films, jobs, or music similar to those that you have already browsed. The Apriori algorithm is the most commonly used for this function.

How does machine learning work?

Machine learning starts with an algorithm for predictive modelling, either self-learnt or programmed that leads to automation. Data science is the means through which we discover the problems that need solving and how that problem can be expressed through a readable algorithm. Supervised machine learning requires either classification or regression problems. 

On a basic level, classification predicts a discrete class label and regression predicts a continuous quantity. There can be an overlap in the two in that a classification algorithm can also predict a continuous value. However, the continuous value will be in the form of a probability for a class label. We often see algorithms that can be utilised for both classification and regression with minor modification in deep neural networks.

Linear regression is when the output is predicted to be continuous with a constant slope. This can help predict values within a continuous range such as sales and price rather than trying to classify them into categories. Logistic regression can be confusing because it is actually used for classification problems. The algorithm is based on the concept of probability and helps with predictive analysis.

Support Vector Machines (SVM) is a fast and much-used algorithm that can be used for both classification and regression problems but is most commonly used in classification. The algorithm is favoured because it can analyse and class even when there is a limited amount of data available. It groups data into classes even when the classes are not immediately clear because it looks at the data three-dimensionally and uses a hyperplane rather than a line to separate it. SVMs can be used for functions like helping your mailbox to detect spam.

How to learn machine learning

With an online MSc Computer Science with Data Analytics or an online MSc Computer Science with Artificial Intelligence from University of York, you’ll get an introduction to machine learning systems and how they are transforming the data science landscape. 

From big data to how artificial neurons work, you’ll understand the fundamentals of this exciting area of technological advances. Find out more and secure your place on one of our cutting-edge master’s courses.


The importance of branding

The word branding originally came from a time when cattle farmers branded their animals with a hot iron to mark their ownership. Each farm or ranch would have its own brand mark usually made up of initials to identify its animals. Although branding and commerce have both grown significantly since then, the idea of the brand logo has not changed much: a simple, bold image that stays in the mind. But a logo is just part of a wider branding exercise that every company should carefully consider.

What is branding in marketing?

Branding is the way a company communicates itself both visually and verbally so that it becomes instantly recognisable to customers. A brand is an intangible concept and yet it forms a very clear idea of what a company does based on its values and identity. Brand identity comprises all visual communications, from the logo design to the typography and colour palette a brand uses on things like its packaging and website. By creating a cohesive identity, the brand experience becomes seamless, and the customer can identify the brand through cues like colour and style before they have even read the brand name. Although brand name is equally important in creating a memorable brand. For instance, the name Amazon recalls the diversity of the rainforest. Amazon.com wanted to become the number one destination for a wide variety of products. In fact, anything that the customer could possibly want, despite originally selling only books – the desire to expand was always there and evident in the name.

Once a visual identity has been decided, a brand guidelines document is usually created to communicate it across the business. By following these guidelines when designing marketing materials, employees and third parties (like a branding agency) can keep the brand’s aesthetics intact across all touchpoints. As well as the brand’s visual style, tone of voice is also very important, representing how the brand speaks. Tone of voice can either be part of the brand guidelines or a separate keystone document.

Why is branding important?

Branding can be the difference between success and failure, all depending on how well it is executed. It may be an area that isn’t given much thought, particularly if a company is hastily created or because founding members feel that things like graphic design are an unnecessary expense. And yet, without the consistency that branding provides, new products or services can easily get lost among competitors with a stronger brand. A powerful brand can create loyal customers, so it’s vital for a new company to think about how it wants to be seen before it launches.

Apple is often cited as a strong brand. It broke the mould on many fronts, from its name and logo having nothing to do with computers (although originally called Apple Computer Company) to its founder’s attitude and personality having a strong effect on the brand’s identity. Although there were originally three co-founders, Steve Jobs eventually drove the brand to prominence, and it was his desire for precision and minimalism that became inextricably linked with the brand. When he died, these design principles were maintained by Chief Design Officer, Jony Ive. Apple was also a change-maker in that its slogan, “Think Different” was purely inspirational and again, did not refer to the product. Other brands which have managed this successfully include Nike, with their “Just Do It” strapline. Both are short, snappy, and aspirational, so that the customer then connects the brand’s products with the chance to live and make real that philosophy for themselves. This creates brand equity, meaning that the brand’s value increases as people begin to perceive the products as being better and more desirable than other brands because of how they make them feel.

Part of brand management is assessing when a company may need a rebrand. This is not uncommon – it can just be a new logo, or it can be an entire rebranding exercise, changing the look and feel of a brand totally. This can be because the brand feels dated, or because the brand’s values have changed. If for example, a brand promise no longer feels relevant or true, updating that one aspect of the branding requires that all aspects be reviewed.

What are branding strategies?

A brand strategy is a document used by all stakeholders in planning the operations of a company. It is a plan that outlines the company’s goals for the brand, one year, three years, five years or further down the line. Activities are planned within the timeline to raise brand awareness among existing and new customers. These tend to be milestone events like new product launches and associated campaigns on social media or through more traditional advertising. Within the brand strategy there will likely be other strategies such as the content strategy, outlining the marketing assets which will be required like blogs, design templates, and copy for social media. The focus of these brand-building activities, especially for marketers, is on creating a brand experience, gaining competitive advantage, and improving financial performance.

As a company expands, it may have multiple products, ranges, and potential sub-brands, all with their own brand strategies. Large, global brands that have expanded over the years sometimes take the decision to unify the brand strategy and simplify communications. This may come after market research shows, for example, that the customer is confused by the differences between the various products, what they offer, and which one is right for them. Coca Cola’s One Brand strategy unites its various products like Diet Coke and Coca Cola Zero Sugar. The products have their own brands and target audiences, but at their core they are all part of the same family.

What is personal branding?

With the rise of social media has emerged the concept of personal branding. Your personal brand is how you present yourself to the world – particularly as a public figure or an expert in your field. 

Increasingly, with social media offering a platform for comment and opinion, many people are public figures whether they intended it or not. Either way, it’s important to think about how you appear to others, what you communicate, and how you communicate it in order to establish a strong personal brand. Someone’s business persona can become a brand in itself or a brand can grow out of a person’s popularity.

What is corporate branding?

Corporate branding is more about pushing the brand as a whole, rather than focusing on products or services. What are a company’s brand values? Does the company have a mission statement that resonates with the times in which we live? These are questions that investors or potential employees may ask. But it also applies to customers who buy into the brand as a whole and are most likely to be early adopters when it comes to any new products the company launches. 

Things that may come under corporate branding that are increasingly important to customers include Corporate Social Responsibility (CSR) and the company’s HR policies. In fact, how a company is perceived by potential employees is down to employer branding, another arm of corporate branding. This includes how the company nurtures its internal culture, how it treats its employees, and how this is communicated.

Learn more about branding in international business

Branding is key to all business and is particularly important to international businesses operating in different territories. Understanding the message that certain logos or words send, as well as the symbolism of particular colours in different cultures, is crucial when operating globally. 

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What is neuromorphic computing?

Compared with first-generation artificial intelligence (AI), neuromorphic computing allows AI learning and decision-making to become more autonomous. Currently, neuromorphic systems are immersed in deep learning to sense and perceive skills used in, for example, speech recognition and complex strategic games like chess and Go. Next-generation AI will mimic the human brain in its ability to interpret and adapt to situations rather than simply working from formulaic algorithms. 

Rather than simply looking for patterns, neuromorphic computing systems will be able to apply common sense and context to what they are reading. Google famously demonstrated the limitations of computer systems that simply use algorithms when its Deep Dream AI was trained to look for dog faces. It ended up converting any imagery that looked like it might contain dog faces into dog faces.

How does neuromorphic computing work?

This third generation of AI computation aims to imitate the complex network of neurons in the human brain. This requires AI to compute and analyse unstructured data that rivals the highly energy-efficient biological brain. Human brains can consume less than 20 watts of power and still outperform supercomputers, demonstrating their unique energy efficiency. The AI version of our neural network of synapses is called spiking neural networks (SNN). Artificial neurons are arranged in layers and each of the spiking neurons can fire independently and communicate with the others, setting in motion a cascade of change in response to stimuli.

Most AI neural network structures are based on what is known as von Neumann architecture – meaning that the network uses a separate memory and processing units. Currently, computers communicate by retrieving data from the memory, moving it to the processing unit, processing data, and then moving back to the memory. This back and forth is both time consuming and energy consuming. It creates a bottleneck which is further emphasised when large datasets need processing. 

In 2017, IBM demonstrated in-memory computing using one million phase change memory (PCM) devices, which both stored and processed information. This was a natural progression from IBM’s TrueNorth neuromorphic chip which they unveiled in 2014. A major step in reducing neuromorphic computers’ power consumption, the massively parallel SNN chip uses one million programmable neurons and 256 million programmable synapses. Dharmendra Modha, IBM fellow and chief scientist for brain-inspired computing, described it as “literally a supercomputer the size of a postage stamp, light like a feather, and low power like a hearing aid.”

An analogue revolution was triggered by the successful building of nanoscale memristive devices also known as memristors. They offer the possibility of building neuromorphic hardware that performs computational tasks in place and at scale. Unlike silicon complementary metal oxide semiconductors (CMOS) circuitry, memristors are switches that store information in their resistance/conductance states. They can also modulate conductivity based on their programming history, which they can recall even if they lose power. Their function is similar to that of human synapses.

Memristive devices need to demonstrate synaptic efficacy and plasticity. Synaptic efficacy refers to the need for low power consumption to carry out the task. Synaptic plasticity is similar to brain plasticity, which we understand through neuroscience. This is the brain’s ability to forge new pathways based on new learnings or, in the case of memristors, new information.

These devices contribute to the realisation of what is known as a massively parallel, manycore supercomputer architecture like SpiNNaker (spiking neural network architecture). SpiNNaker is the largest artificial neural network using a million general purpose processors. Despite the high number of processors, it is a low-power, low-latency architecture and, more importantly, highly scalable. To save energy, chips and whole boards can be switched off. The project is supported by the European Human Brain Project (HBP) and its creators hope to model up to a billion biological neurons in real time. To understand the scale, one billion neurons is just 1% of the scale of the human brain. The HBP grew out of BrainScaleS, an EU-funded research project, which began in 2011. It has benefitted from the collaboration of 19 research groups from 10 European companies. Now with neuromorphic tech evolving fast, it seems the race is on. In 2020, Intel Corp announced that it was working on a three-year project with Sandia National Laboratories to build a brain-based computer of one billion or more artificial neurons.

We will see neuromorphic devices used more and more to complement and enhance the use of CPUs (central processing units), GPUs (graphics processing units) and FPGA (field programmable gate arrays) technologies. Neuromorphic devices can carry out complex and high-performance tasks – for example, learning, searching, sensing – using extremely low power. A real-world example would be instant voice recognition in mobile phones without the processor having to communicate with the cloud.

Why do we need neuromorphic computing?

Neuromorphic architectures, although informed by the workings of the brain, may help uncover the many things we don’t know about the brain by allowing us to see the behaviour of synapses in action. This could lead to huge strides in neuroscience and medicine. Although advances in neuromorphic processors that power supercomputers continue at unprecedented levels, there is still some way to go in achieving the full potential of neuromorphic technology.

A project like SpiNNaker, although large-scale, can only simulate relatively small regions of the brain. However, even with its current capabilities, it has been able to simulate a part of the brain known as the Basal Ganglia, a region that we know is affected in Parkinson’s Disease. Further study of the simulated activity with the assistance of machine learning  could provide scientific breakthroughs in understanding why and how Parkinson’s happens.

Intel Labs is a key player in neuromorphic computer science. Researchers from Intel Labs and Cornell University were able to use Intel’s neuromorphic chip, known as Loihi, so that AI could recognise the odour of hazardous chemicals. Loihi chips use an asynchronous spiking neural network to implement adaptive fine-grained computations in parallel that are self-modifying, and event driven. This kind of computation allows this level of odour recognition even when surrounded by ‘noise’ by imitating the architecture of the human olfactory bulb. The neuroscience involved in the sense of smell is notoriously complex, so this is a huge first for AI and wouldn’t be possible with the old-style transistors used in processing. This kind of discovery could lead to further understanding around memory and illnesses like Alzheimer’s, which has been linked to loss of smell.

Learn more about neuromorphic computing and its applications

Artificial intelligence is already helping us to make strides in everyday life from e-commerce to medicine, finance to security. There is so much more that supercomputers could potentially unlock to help us with society’s biggest challenges. 

Interested to know more? Find out about the University of York’s MSc Computer Science with Artificial Intelligence.