Importantly, each of these fundamentally data-driven processes draws on the findings obtained through the analysis and evaluation of statistical data to help inform strategic decision-making. The skills required for each of these professions are also very comparable. From mathematical abilities to critical and analytical thinking – the foundational requirements to perform these roles are almost the same. There are, however, some key differences – which lie mostly in the way each of these processes is executed.

Stay with us as we explore the main differences between business analytics and data science, as well as the key characteristics of each concept.

What is Business Analytics?

Essentially, business analytics is the process by which business data, statistics, and internal processes are analyzed, assessed, and evaluated.  A skilled business analyst’s ultimate goal is to assist a business in making better, more strategic decisions, as well as solving issues and problems that can be avoided by making data-informed choices. 

Business analytics can be both a lucrative and rewarding career. If you are interested in becoming a business analyst, however, you will need to complete a tertiary qualification – such as an online Masters in Business Analytics, for example. 

Completing this type of degree can prove to be well worth the effort, though. With more and more businesses today becoming increasingly data-driven, the services of an individual who can skillfully perform business analytics are currently in high demand. As such, if you complete a qualification as a business analyst, the career outcomes that are available to you will be abundant! Of course, you also will need to possess the necessary skills and knowledge required to be a business analyst. Some of these skills include analytical and critical thinking, mathematical skills, and of course, problem-solving abilities. 

What is Data Science?

Data science leans on computer programs and software to analyze data and statistics. As such, it is heavily focused on computer science and programming. For this reason, data scientists need to be highly computer literate and adept at using programming software.

So, how to become a data scientist? As well as completing a tertiary qualification in the discipline of data science, it is also important to build a portfolio of your work. This will enable you to become professionally employed as a data science consultant. Of course, just like a business analyst, data scientists must also have strong mathematical abilities – including skills in algebra and calculus. Being a data-driven role, a successful data scientist must also have strong analytical and critical thinking capabilities, as well as the ability to accurately analyze and assess statistics.

There are however some key differences between the two professions. To learn more about the differences between business analytics and data science, just keep reading!

What are the Differences between Business Analytics and Data Science?

Admittedly, there are some significant similarities between the role of a business analyst and that of a data scientist. These similarities mainly relate to the skills required of an individual who chooses to pursue a career in either field. That is to say, data scientists and business analysts must both be competent in math, and skilled in data and statistical analysis. There are, however, some differences – although, these can be somewhat difficult to define! For this reason, entire schools of thought have been dedicated to discussing data science versus business analytics. 

Essentially, the main differences between the two lie in the outcomes of each of these processes. For instance, business analytics uses data analysis to identify and resolve business-orientated problems and issues. Data science, on the other hand, draws on algorithms to pre-empt and predict these issues ahead of time. For this reason, data science is considered by some experts to be more effective than business analytics.

Furthermore, data science is a somewhat more modern approach to data analysis and evaluation. Data science, as a concept, was only founded in 2008. Business analytics, on the other hand, has been around since the late 19th century. For this reason, data science draws on more advanced technology for its approach and leans heavily on computer software and analysis of digital algorithms to execute its methods.

Unlike several years ago, students can connect with each other and their teachers online, through video calls, and even with tools like virtual reality. 

As a result of this rapidly developing technology, learning and developing new skills has become cheaper, faster, and more effective. Students are becoming less interested in pursuing a typical college education, and more interested in taking advantage of new ways of getting more training and education. 

Let’s take a look at some of the companies that are helping to change the way the world learns. 

Maven

Maven is a completely online learning platform with hundreds of different courses to choose from. Experts in the fields of artificial intelligence, marketing, business, leadership, design, writing, data, and more use Maven to build their own course and reach people all over the world who are interested in what they have to say. 

Maven courses range in price from $200 to over $1,000, depending on the content and the course creator. As students attend different courses, they are able to leave reviews and feedback for other students interested in taking the course. Courses are delivered live via online video recording and can contain anywhere from several hours to several days’ worth of content. 

Teachable

Teachable is another platform for experts to create their own lessons and share them with a larger audience. Teachable helps content creators organize lessons on everything from yoga to finance, and range from free video lessons to weeks-long courses costing several thousand dollars. 

Many influencers on social media use teachable to organize their lessons into more understandable content for their consumers. Teachable also helps these influencers learn marketing, organization, and product sales strategies.

In addition to the lessons available through Teachable, users can subscribe to the community of teachers and learners in Teachable:HQ. This is a place where people can come together to ask questions and get specific tips about how to expand their audience and grow their business. 

Udemy

Udemy has tens of thousands of courses where users can learn anything from a second language, to how to grill a steak, to how to improve their small business sales. Udemy is unique because it offers subscription plans for individuals and groups that allow the user access to thousands of courses for as long as the subscription is active. 

Individual subscriptions start at $16.99 per month for access to 10,500 courses, and group subscriptions start at $30 per person, per month for access to over 24,000 courses. 

In addition to the subscription services, users have the option of purchasing individual courses. These courses typically cost between $100-$200 and can be completed in a few hours of work. 

Stepful

Stepful offers online learning and certification opportunities for those who are interested in starting a career in healthcare. Their medical assisting and pharmacy technician certification courses prepare students to pass the CCMA and CPhT exams, respectively. These certifications are accepted by states that require certification and are recognized by healthcare organizations across the nation. 

Training programs through Stepful are designed to allow students to complete their learning online, and then have an opportunity to get hands-on experience through an externship. Stepful not only has an 85% certification rate for both of their programs, but they also offer a job placement guarantee. 

Stepful courses can be completed in four months and cost $1,999 each. 

While ChatGPT deals in words and sentences, the team’s AI will learn from numerical data and physics simulations from across scientific fields to aid scientists in modelling everything from supergiant stars to the Earth’s climate.

The team launched the initiative, called Polymathic AI earlier this week, alongside the publication of a series of related scientific papers on the arXiv.org open access repository.

“This will completely change how people use AI and machine learning in science,” said Polymathic AI principal investigator Shirley Ho, a group leader at the Flatiron Institute’s Center for Computational Astrophysics in New York City.

The idea behind Polymathic AI “is similar to how it’s easier to learn a new language when you already know five languages,” said Ho.

Starting with a large, pre-trained model, known as a foundation model, can be both faster and more accurate than building a scientific model from scratch. That can be true even if the training data isn’t obviously relevant to the problem at hand.

“It’s been difficult to carry out academic research on full-scale foundation models due to the scale of computing power required,” said co-investigator Miles Cranmer, from Cambridge’s Department of Applied Mathematics and Theoretical Physics and Institute of Astronomy. “Our collaboration with Simons Foundation has provided us with unique resources to start prototyping these models for use in basic science, which researchers around the world will be able to build from – it’s exciting.”

“Polymathic AI can show us commonalities and connections between different fields that might have been missed,” said co-investigator Siavash Golkar, a guest researcher at the Flatiron Institute’s Center for Computational Astrophysics. “In previous centuries, some of the most influential scientists were polymaths with a wide-ranging grasp of different fields. This allowed them to see connections that helped them get inspiration for their work. With each scientific domain becoming more and more specialised, it is increasingly challenging to stay at the forefront of multiple fields. I think this is a place where AI can help us by aggregating information from many disciplines.”

The Polymathic AI team includes researchers from the Simons Foundation and its Flatiron Institute, New York University, the University of Cambridge, Princeton University and the Lawrence Berkeley National Laboratory. The team includes experts in physics, astrophysics, mathematics, artificial intelligence and neuroscience.

Scientists have used AI tools before, but they’ve primarily been purpose-built and trained using relevant data. “Despite rapid progress of machine learning in recent years in various scientific fields, in almost all cases, machine learning solutions are developed for specific use cases and trained on some very specific data,” said co-investigator Francois Lanusse, a cosmologist at the Centre national de la recherche scientifique (CNRS) in France. “This creates boundaries both within and between disciplines, meaning that scientists using AI for their research do not benefit from information that may exist, but in a different format, or in a different field entirely.”

Polymathic AI’s project will learn using data from diverse sources across physics and astrophysics (and eventually fields such as chemistry and genomics, its creators say) and apply that multidisciplinary savvy to a wide range of scientific problems. The project will “connect many seemingly disparate subfields into something greater than the sum of their parts,” said project member Mariel Pettee, a postdoctoral researcher at Lawrence Berkeley National Laboratory.

“How far we can make these jumps between disciplines is unclear,” said Ho. “That’s what we want to do — to try and make it happen.”

ChatGPT has well-known limitations when it comes to accuracy (for instance, the chatbot says 2,023 times 1,234 is 2,497,582 rather than the correct answer of 2,496,382). Polymathic AI’s project will avoid many of those pitfalls, Ho said, by treating numbers as actual numbers, not just characters on the same level as letters and punctuation. The training data will also use real scientific datasets that capture the physics underlying the cosmos.

Transparency and openness are a big part of the project, Ho said. “We want to make everything public. We want to democratise AI for science in such a way that, in a few years, we’ll be able to serve a pre-trained model to the community that can help improve scientific analyses across a wide variety of problems and domains.”

Source: University of Cambridge

According to a study, among B2B customers, 71% are at risk of defecting to competition, and only 29% are genuinely engaged. You can keep the customers engaged by devising ways that have a positive impact on their bottom line.

QBRs (Quarterly Business Review) presentations are an excellent way to showcase those metrics and depict the evaluation of whether the company has stayed on track regarding meeting the goals and requirements of clients.

However, despite the importance, the QBR presentations can be dreaded by the leadership sometimes. There could be a lot of reasons like the presentations are long, unfocussed among other things.

This article will help you figure out why your QBR presentations are failing and what you should avoid to convey your reflections clearly.

12 Reasons for Failing QBR Presentations

Your QBR presentation helps you present the previous quarter’s performance and the roadmap for the upcoming quarter. These presentations provide you an opportunity to brainstorm what went well, what activities should be stopped, and what tasks should be continued. All of this makes the QBR presentation important.

However, there are a few slip-ups that must be taken care of to ensure a successful presentation–

1. Lacking focus, structure, purpose, or plan

Your review presentation should be concise and on point. It should ideally focus on the key performance goals and expand upon the developments over the quarter. The review presentation often frustrates customers because they lack structure and aren’t organized.

There isn’t a clearly defined agenda, and worse, sometimes the presentation deviates entirely from the agenda in its quest to cover a lot of content.

Instead – Have a clearly defined agenda with fitting timelines (for everything) that ensures the proper flow and coverage of everything essential.

2. You are not really upfront

You might be tempted to present facts a little less harshly by sugar coating them. But this won’t serve the required purpose. The presentation is not for detailing the success story. Instead, it is for showing the analysis of the progress made so far and discussing action plans for the upcoming quarter.

Honesty will help you build trust and involve everyone to brainstorm ideas for the future.

Instead – Be upfront and explain the context of the failures.

3. Too boring to handle

The format followed is too lecturing style, which results in participants getting bored with the presentation.

The traditional presentation format doesn’t inspire participation and engagement from the audience, stifling the chances of any active discussions for better winning ideas for the future.

Instead – Use multimedia content like catchy graphics and visuals and pre-designed templates to pique curiosity and engagement.

4. There aren’t definite conclusions

It is good to be reflective and explain tactics, but it is not good to be inconclusive with critical benchmarks. You must be conclusive for future campaigns, clearly elaborating on the opportunities and challenges.

For instance, let’s say the target is to increase sales for the buyer.

The campaign led to that (Strength), but the website crashed due to poor loading capacity (Weakness).

The influx of new buyers (new business) means a chance to upgrade the website (Opportunity) but also the risk of overwhelming support calls if not taken care of by the customer success department (Threat).

Instead – Do a SWOT Analysis to provide insights into the strengths, weaknesses, opportunities, and threats.

5. Working with too many KPIs

Analytics and data are essential to give a clear picture of the scenario. However, be wise not to get carried away with too many KPIs.

Focusing on too many parameters will result in customers getting lost in the data. Use KPIs that show the success of your strategies, like customer churn rate and retention costs, Net Revenue Retention (NRR), upsells/cross-sells, customer health, etc.

In these, churn rate and NRR are key KPIs to highlight the bottom line of the business and the parameters to focus on.

Instead – Pick KPIs the clients understand clearly and are essential for their bottom line.

6. Not using real data

It is not a good strategy to simply point to the campaigns that were successful and those that were not. Real data gives credibility to the material and findings.

Statistics will provide concrete evidence of your strategies (whether working or not) and illustrate which factors have been affecting your success (time and method).

Having said that, don’t focus too much on the numbers and end up making the presentation boring and robotic.

Instead – Use hard numbers to explain your results concretely and concisely. Take the help of data visualizations and infographics to make the information more engaging and easy to grasp.

7. On-ground support missing

One of the reasons QBR presentations fail is that they don’t get the key stakeholders on board. The right people, even if they are not a part of the presentation, need to have access to the data and the overall process and follow up when needed.

You don’t have all the answers, and missing out on the right people will result in missing out on the key factors that might affect your success plan.

For example, for accounts matter, you will need to have people involved in purchase decisions on board. Similarly, if you are working on sales strategies, you will need to speak to the required sales personnel.

Instead – Involve relevant people for valuable insights into the process.

8. No roadmap

The QBR presentations must not be restricted solely to the discussion of the performance of the past 90 days. It is wise to look upon the reflections and devise strategies for the future.

By brainstorming what strategies worked and what didn’t, you get the building blocks to create goal-oriented and actionable steps for the next quarter. By assessing the current scenario, you learn everything that will impact your planned trajectory.

Instead – Try to devote at least 1/4th of the presentation time to discussing future plans.

9. Not caring enough about the attendees

The QBR presentations fall flat if they are too much about resting on one’s laurels.

As your attendees are devoting their precious time to listening to what you have to say, they want the presentation to be truly valuable for them.

Instead – Make your presentation audience-centric and encourage their active participation. Listen carefully to their responses/feedback and take appropriate notes.

10. Too long

QBR presentations last for long hours generally. If you add in the preparation time or the travel as well, the time invested in the process becomes a lot. Since the presentation is quarterly, the process amounts to 4 times a year. It translates to a lot of time invested by the parties involved, which could have been spent in actualizing the goals.

Instead – Limit the presentation duration to a fixed time, with presenters getting a stipulated time to present the material and not more than that.

11. An expensive exercise

Travel expenses, presentation material, refreshments, printing costs, lodging expenses, off-site meeting costs, etc., add to it the long list of people who are supposed to attend this every 3-month presentation.

All this can make the presentation exercise a little heavy on the pocket.

Instead – You can conduct a couple of your presentations online to cut down on time and other expenses.

12. Being too technical and complex

You lose the essence of the presentation by making it technical. Technical jargon, acronyms, and overuse of data and metrics might make the presentation too complex for people to understand.

Also, you should refrain from sharing obscure data or making your presentation like a dissertation.

Instead – Include a succinct summary with simple graphs, bullet points, figures, infographics, etc., that makes the key point digestible. Keep your presentation shareable, concise, and as simple as possible.

Summing It Up

A Quarterly Business Review presentation depicts a periodic review of last quarter’s progress and showcases actionable insights for the next quarter. However, this presentation fails to make the required impact due to the factors mentioned above.

Be mindful of keeping the presentation concise, organized, customer-centric, and goal-oriented. Use graphs, charts, illustrations, etc., to enhance the visualization and meaning of the content.

Let us know your thoughts in the ‘comment’ section below!

Covering Low Costs

Online academic conferences are cheaper since they eliminate hiring venues, catering services, or accommodation. Attendees require a stable Internet connection and a suitable device. Besides, the host eliminates installing audio-visual equipment to facilitate the cost. In most cases, hosts can conduct the seminar from the comfort of their homes or offices. 

Providing Unlimited Attendees 

Online academic conferences allow people to host large participants. Some virtual communication platforms allow people to host up to 1000 attendees. Using such online services to maintain an academic seminar allows hosts to avoid possible attendee restrictions when renting small venues. In practice, people register and attend the seminar from their homes or offices. Besides, this feature is crucial during the ongoing pandemic where people require to maintain social distances to avoid infections. 

Eliminating Delegates

Face-to-face academic conferences require people to travel and complete many registrations. Online academic conferences eliminate these challenges by allowing attendees to participate from their offices. In this case, attendees do not have to travel to a specific venue. Instead, they generally pay lower tickets compared with the traditional conference. Besides, several members from an institution may attend the conference without interfering with their operations or duties.    

Enhancing Inclusivity

Virtual academic conferences allow geographically dispersed delegates and opportunity to attend. For instance, people from different countries cannot attend face-to-face conferences due to cost and travel constraints. However, digital technologies enable hosts to use translations and captions to increase user-friendliness and inclusivity. In this case, users from different cultural backgrounds can attend virtual academic seminars without experiencing language barriers.    

Reducing Environmental Pollution

Face-to-face academic seminars contribute to environmental waste. For instance, travel for staff, delegates, speakers, food waste, single-use exhibition buildings, freights, and name badges leave significant environmental footprints. However, online conferences eliminate such factors since people participate from their homes or offices. They can reduce their carbon footprint by hosting academic seminars online.  

Promoting Flexibility and Convenience 

Online academic seminars are shorter than conventional face-to-face conferences. Attendees experience flexible access to pre-recorded content at their workplaces. Such approaches allow people to balance commitments, like work, study, and childcare, while enhancing their knowledge. 

Creating Easy Networking

Online seminars allow delegates to easily connect with keynote speakers, exhibitors, and other attendees. For instance, a delegate requires to make a few mouse clicks to switch from one speaker to another. Such abilities reduce the need for delegates to run through crowded exhibition halls in their attempts to meet specific individuals of interest. In conventional face-to-face academic seminars, one may miss people that they intended to interact with due to congestions and commitments.  

Improving Analytics 

Virtual conference platforms have inbuilt sophisticated data collection and reporting tools. For instance, people who use Internet-based communication tools can easily track leads for sponsors and identify those who engaged with the content provided. Besides, they can determine how long each user interacted with specific content. Such information is useful in enabling online conference hosts to make evidence-based decisions regarding the success of their meetings. 

Strengthening Professional Ties 

Online academic conferences bring together scholars from different countries. Academics from different cultural backgrounds socialize to grow their professional networks and strengthen their bonds. The short breaks offered during online conferences provide unique opportunities for scholars to meet new mentors or associates. Besides, attendees connect with people outside their social circles while establishing influential connections. 

Allowing Constructive Feedback

Online academic conferences allow all users to provide real-time feedback. For instance, critiques and constructive feedback are invaluable in allowing people to appreciate and overcome their weaknesses. Online seminars ensure that hosts gather criticism and suggestions from many attendees. In this case, hosts can utilize the feedback to improve the quality of future seminars. Besides, real-time feedback ensures that seminar hosts and speakers enhance their research on specific topics. 

Conclusion 

Online academic conferences are in the early stage of development but are invaluable tools for communicating important ideas. People can use online academic platforms to reduce costs, attract unlimited attendees, and eliminate the need for delegates. The effective use of Internet-based academic seminars improves inclusivity, reduces environmental pollution, and enhances flexibility and convenience for attendees. Besides, these virtual events create easy networking, improve analytics, strengthen professional ties, and allow hosts and speakers to receive constructive feedback.

But what is artificial intelligence in education? This sector is another industry that has and is still benefiting drastically from AI. Artificial intelligence in teaching and learning is taking over the traditional method of passing information to students. It has helped impart knowledge and provide an easier understanding. In a short time since AI’s presence, it has drastically changed the education system.

The Role of Artificial Intelligence in Education

The question of what AI is in education is answered by highlighting the importance of technology in the academic sector. Below are highlights of the use of Artificial intelligence in education:

Providing Efficient Learning and Writing Service 

The impact of artificial intelligence on learning is second to none. These days, students have access to numerous data with the use of just their smart devices. They attend classes and search for information to learn more. Professional essay writing services from Edusson help you with research papers, essays, and other assignments. It makes your work easier and gives them more time to focus on other essential aspects of the curriculum. Students who love to multitask, probably working a part-time job or joining the school team, benefit significantly from professional writers and the power of AI. As their instructors teach anytime, they learn anytime with unlimited materials, resources, and textbooks.

Making Teaching Easier

In recent times, lecturers do not need to come up with taking lesson notes and look for the best way to present them to several students who might need help understanding the topic. Artificial intelligence has made it easy by providing a platform where lesson notes are uploaded and delivered to learners in a personalized manner.

This way, the lecturer can teach every pupil individually without even having to come in contact with them. So whether the one learns fast or slow, the lecturer can take each at their own pace. It is the work of the AI. They sit and revise lectures as often as possible until they are satisfied with their knowledge.

Accessible Education to All

They say educating yourself is the key to success. But some individuals need the financial ability to purchase that key. Now, Universities are starting to shift some of their programs online. If you don’t have the resources to take you through high school, you earn a degree with your phone. Best believe the doubts are there, but AI has made such an impact that even the poorest person has unlimited access to information to improve them. Even those who can afford to go to school will cut costs as things like textbooks and lecturer’s handouts are now found online. As artificial intelligence improves, more courses will be available online for students to take for free. Offering them the free key to success.

Assist in Administrative Work

The students and everybody in the school system feel the joy of AI. Undoubtedly, teachers are excited that it does much of their work. This part of the work is the making and grading of individuals. A lecturer who takes about fifty learners needs to sit down to grade and assess each one after the other—trying their best not to make mistakes. Technology changes the face of education, and even teachers testify. These days, they only need to run the assignments through the computer after providing the necessary information. All that is left is for the computer to do the task quickly. This advancement assesses faster and more accurately. Simultaneously, it relieves the lecturers and grants them more time with their students.

Choosing the Right School and Course for Students

Undoubtedly, most students who want to go to college choose institutions that have made a name for themselves. On the other hand, these institutions only grant some admission if they limit the number of learners they accept. Private and other not-so-popular institutions on the other end of the spectrum have classes with empty seats and no one to fill them up. How is AI used in education in this concept? It is simple. It plays the matchmaker role.

When prospective applicants wish to apply to a school, they have the AI consult to guide them on their qualifications and the chances of getting into their dream school. It also provides other options that fit their skill set. On the other hand, the administrative sector of schools can use computer algorithms to screen each learner. It makes it easier for them to admit applicants who fit into their ideal picture of one.

Positive and Negative Impact of AI in the Education Sector

Like the two sides of a coin, every advancement has advantages and disadvantages. There are pros and cons of AI in education listed below:

PROS

The advantages of using artificial intelligence in education are numerous. Some of them include:

• Access to unlimited resources.
• Provision of organized information.
• Providing empowerment to students with special needs.
• Cost savings.
• Increased learning experience.
• Increased academic standards.

CONS

The disadvantages of AI in education are fewer when compared to the advantages. Nonetheless, the advancement of technology will make it better day after day. The cons include:

• Displacement of academic and nonacademic lecturers from their jobs.
• Lack of emotional intelligence between students and machine learning, as it is with teachers.
• Lack of privacy and security as automated systems have access to much personal information.
• Maintenance challenges.
• Inequality and bias when helping young people make school choices.
• Lack of discipline among learners

Wrapping Up

From assisting in writing research papers and other academic tasks, artificial intelligence has played a crucial role in making teaching and learning easy. The technology aims at making the world a better place, so most of the disadvantages of machine learning may be solved within a period. As students, it provides more good than harm. Own your self-discipline and use every resource, video content, and graphical illustration to your success. Professors, on the other hand, should ensure they monitor the work and pace of each learner by grading and accessing them regularly. Technology is here to make life easier for everybody.

Our brains are very complex organs, with huge variety between individuals in terms of the overall volume of the brain, how it is folded and how thick these folds are. Little is known about how our genetic make-up shapes the development of the brain.

To answer this question, a team led by researchers at the Autism Research Centre, University of Cambridge, accessed MRI scans from over 32,000 adults from the UK Biobank cohort and over 4,000 children from the US-based ABCD study. From these scans, the researchers measured multiple properties of the outermost layer of the brain called the cortex. These included measures of the area and volume of the cortex as well as how the cortex is folded.

They then linked these properties, measured both across the entire cortex as well as in 180 individual regions of the cortex, to genetic information across the genome. The team identified over 4,000 genetic variants linked to brain structure.

These findings have allowed researchers to confirm and, in some cases, identify, how different properties of the brain are genetically linked to each other.

Dr Varun Warrier from the Autism Research Centre, who co-led the study, said: “One question that has interested us for a while is if the same genes that are linked to how big the cortex is – measured as both volume and area – are also linked to how the cortex is folded. By measuring these different properties of the brain and linking them to genetics, we found that different sets of genes contribute to folding and size of the cortex.”

The team also checked whether the same genes that are linked to variation in brain size in the general population overlap with genes linked to clinical conditions where head sizes are much larger or smaller than the general population, known as cephalic conditions.

Dr Richard Bethlehem, also from the Autism Research Centre and a co-lead of the study, said: “Many of the genes linked with differences in the brain sizes in the general population overlapped with genes implicated in cephalic conditions. However, we still do not know how exactly these genes lead to changes in brain size.”

Dr Warrier added: “This work shows that how our brain develops is partly genetic. Our findings can be used to understand how changes in the shape and size of the brain can lead to neurological and psychiatric conditions, potentially leading to better treatment and support for those who need it.”

This study was supported by the Wellcome Trust. It was conducted in association with the NIHR CLAHRC for Cambridgeshire and Peterborough NHS Foundation Trust, and the NIHR Cambridge Biomedical Research Centre.

Source: University of Cambridge

Experts from the University of Nottingham’s School of Computer Science have been awarded almost £700,000 funding from EPSRC to lead a project to enhance understanding of SMEs’ cyber security support needs and their ability to address them.

The research aims to establish pilot Cyber Security Communities of Support (CyCOS), bringing together SMEs and advisory sources for practical help and support.  The UK Cyber Security Breaches Survey indicates that half of small and a third of micro businesses experienced breaches or attacks in the last year. Whilst they do seek external guidance in relation to cyber security, they do so via a huge range of sources, and often find themselves overwhelmed with information and unable to understand the advice.

The research team includes Dr Maria Bada from Queen Mary University of London and Dr Jason Nurse from the University of Kent and is led by Steven Furnell, Professor of Cyber Security at the University of Nottingham.

Businesses know there is a need to protect themselves from cyber attacks, but knowing just how to do this and where to go for trusted help can be a minefield. We want to make the process of accessing help easier and more targeted. Our research will improve understanding of SME needs and the perspective of those that they turn to for support. We will then use these insights as a foundation for the design and evaluation of a new and more accessible model for support with the Communities of Support pilots.

Steven Furnell, Professor of Cyber Security at the University of Nottingham

The research will investigate the support needs of small businesses, to establish their current understanding and confidence around cyber security, and their awareness and perceptions of available support. The investigation will seek to determine the scenarios in which cyber security advice is sought (e.g. during product evaluation, at point of purchase, in response to threats and incidents), and whether it is deemed effective.

The project will also analyse support routes available to these businesses, focusing on the coverage and consistency of advice, as well as the confidence and capacity of those providing it.

Research findings will be used to establish three pilot CyCOS which will include the creation of an online Support Broker, enabling the SMEs to identify support needs and contact advisory sources positioned to help them (which, as the community develops and grows in experience, may include peer support from other SMEs). The project offers upskilling opportunities for advisors and interested SMEs, via foundational cyber security certification to increase their related knowledge and capability.

The research is supported by strong industry collaboration, with partners including the Home Office, (ISC)2, IASME, the Chartered Institute of Information Security, the Centre for the New Midlands, and three regional Cyber Resilience Centres.

Professor Furnell adds: “This project is an exciting opportunity to plug a gap that exists in cyber security for SME’s. We hope that if successful the CyCOS model could be rolled out nationally and become a vital tool in the fight against cyber attacks.”

Source: University of Nottingham

Human error and uncertainty are concepts that many artificial intelligence systems fail to grasp, particularly in systems where a human provides feedback to a machine learning model. Many of these systems are programmed to assume that humans are always certain and correct, but real-world decision-making includes occasional mistakes and uncertainty.

Researchers from the University of Cambridge, along with The Alan Turing Institute, Princeton, and Google DeepMind, have been attempting to bridge the gap between human behaviour and machine learning, so that uncertainty can be more fully accounted for in AI applications where humans and machines are working together. This could help reduce risk and improve trust and reliability of these applications, especially where safety is critical, such as medical diagnosis.

The team adapted a well-known image classification dataset so that humans could provide feedback and indicate their level of uncertainty when labelling a particular image. The researchers found that training with uncertain labels can improve these systems’ performance in handling uncertain feedback, although humans also cause the overall performance of these hybrid systems to drop. Their results will be reported at the AAAI/ACM Conference on Artificial Intelligence, Ethics and Society (AIES 2023) in Montréal.

‘Human-in-the-loop’ machine learning systems – a type of AI system that enables human feedback – are often framed as a promising way to reduce risks in settings where automated models cannot be relied upon to make decisions alone. But what if the humans are unsure?

“Uncertainty is central in how humans reason about the world but many AI models fail to take this into account,” said first author Katherine Collins from Cambridge’s Department of Engineering. “A lot of developers are working to address model uncertainty, but less work has been done on addressing uncertainty from the person’s point of view.”

We are constantly making decisions based on the balance of probabilities, often without really thinking about it. Most of the time – for example, if we wave at someone who looks just like a friend but turns out to be a total stranger – there’s no harm if we get things wrong. However, in certain applications, uncertainty comes with real safety risks.

“Many human-AI systems assume that humans are always certain of their decisions, which isn’t how humans work – we all make mistakes,” said Collins. “We wanted to look at what happens when people express uncertainty, which is especially important in safety-critical settings, like a clinician working with a medical AI system.”

“We need better tools to recalibrate these models, so that the people working with them are empowered to say when they’re uncertain,” said co-author Matthew Barker, who recently completed his MEng degree at Gonville & Caius College, Cambridge. “Although machines can be trained with complete confidence, humans often can’t provide this, and machine learning models struggle with that uncertainty.”

For their study, the researchers used some of the benchmark machine learning datasets: one was for digit classification, another for classifying chest X-rays, and one for classifying images of birds. For the first two datasets, the researchers simulated uncertainty, but for the bird dataset, they had human participants indicate how certain they were of the images they were looking at: whether a bird was red or orange, for example. These annotated ‘soft labels’ provided by the human participants allowed the researchers to determine how the final output was changed. However, they found that performance degraded rapidly when machines were replaced with humans.

“We know from decades of behavioural research that humans are almost never 100% certain, but it’s a challenge to incorporate this into machine learning,” said Barker. “We’re trying to bridge the two fields so that machine learning can start to deal with human uncertainty where humans are part of the system.”

The researchers say their results have identified several open challenges when incorporating humans into machine learning models. They are releasing their datasets so that further research can be carried out and uncertainty might be built into machine learning systems.

“As some of our colleagues so brilliantly put it, uncertainty is a form of transparency, and that’s hugely important,” said Collins. “We need to figure out when we can trust a model and when to trust a human and why. In certain applications, we’re looking at probability over possibilities. Especially with the rise of chatbots, for example, we need models that better incorporate the language of possibility, which may lead to a more natural, safe experience.”

“In some ways, this work raised more questions than it answered,” said Barker. “But even though humans may be miscalibrated in their uncertainty, we can improve the trustworthiness and reliability of these human-in-the-loop systems by accounting for human behaviour.”

The research was supported in part by the Cambridge Trust, the Marshall Commission, the Leverhulme Trust, the Gates Cambridge Trust and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

Source: University of Cambridge

“In a world overflowing with data, unlocking its power sets you apart. Data literacy is more relevant than ever for young learners and professionals wishing to upskill and reskill,” said Dr. Emily Pressman, who developed the SAS Data Literacy series. “Our courses are designed to be easily consumed by learners and integrated into instruction and professional development offerings.”

Whether a student, educator or professional, everyone can benefit from better data literacy. Educators can better prepare students for careers, while employers can improve the data literacy of their workforces. Business professionals can enhance their decision making through data analysis, and individuals can explore the power of data for personal and professional enrichment.

Key features make building relevant data literacy easy

Data Literacy in Practice makes learning about data fun, easy and practical. Key features include:

• User-friendly design – The course is for everyone, no matter where they are on their data literacy journey. By focusing on conceptual and practical understanding rather than distracting mathematical jargon, learners feel supported and encouraged throughout the process.
• Real-world examples – Data Literacy in Practice incorporates real-world examples, putting course takers in the shoes of a small business owner who utilizes data to drive informed decision making. By working through practical scenarios, learners develop the skills to apply data literacy knowledge in real-life situations.
• Convenient and self-paced – SAS Data Literacy e-learning courses offer the flexibility to learn at any pace, anytime and anywhere. With modularized content accessible on any device, users can easily fit learning into their schedules. The self-paced course can be completed in under three hours.

Part of a powerful data literacy duo

While Data Literacy in Practice can stand alone, it is even more powerful when combined with the free SAS Data Literacy Essentials course. Starting from the basics, Data Literacy Essentials introduces learners to fundamental data literacy concepts, such as what data is and why data literacy matters.

Data Literacy Essentials has been activated about 20,000 times in nearly 80 countries by more than 600 colleges and universities and 3,500 nonacademic organizations, including small businesses, large companies, government agencies and others. Users include hundreds of higher education faculty, including Paul Hansford, Associate Professor in the Business and Public Services Division at Sinclair Community College.

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By combining the knowledge gained from Data Literacy Essentials with the practical data visualization and exploration skills acquired in Data Literacy in Practice, learners will be fully equipped to embark on the next step toward data literacy: SAS® Visual Analytics 1. This three-course learning pathway of Data Literacy Essentials, Data Literacy in Practice and SAS Visual Analytics 1 sets learners on their analytics career journey.

Data literacy is a key to business resilience

In a time of global disruptions, resiliency determines which organizations struggle and which survive and thrive. In March, SAS released a global business survey report, Resiliency Rules, which explores the current state of business resiliency and what steps organizations are taking to navigate change and seize opportunity.

The survey of more than 2,400 business executives revealed a trend that high-resiliency organizations have a strong focus on data culture and literacy. More than having a tool for data management, resilient organizations create a culture of looking to data to help solve problems, answer questions and make decisions.

Source: SAS