causaLens is at the forefront of Causal AI innovation, providing solutions through the Microsoft marketplace that help organisations move beyond existing predictive models.  

In this third of our four-blog series, we’ll explore how causaLens has productised Causal AI to reimagine problem-solving across supply chains, customer journeys, manufacturing processes and more.

AI that answers your business questions 

“If I increase the price of my product by 5%, how will that affect my customer retention rate and revenue?”  

Standard machine learning struggles to provide answers to certain business questions, due to its tendency to forecast outcomes. The power of Causal AI models, which are fully transparent and explainable, is that they capture underlying causal relationships in data, not mere data correlations. This enables them to respond confidently to hypotheticals and provide actionable recommendations to optimise your business KPIs.  

While recent years have seen growing academic interest in this emerging technology, causaLens is the only company to have productised it through the decisionOS platform. 

Workflows built to answer “what-ifs” 

decisionOS lets you build and deploy decision workflows leveraging Causal AI – going from raw data to a production-ready decision-making application seamlessly. Data scientists can use decisionOS to access the latest Causal AI algorithms and build best in class Causal AI models. Business users can access “decisionApps” deployed within decisionOS to surface actionable recommendations for their use-cases and improve decision-making.  

Using decisionOS, your team is empowered to build models that go beyond correlations and understand cause and effect relationships that can predict accurately, but, more importantly, assess the impact of interventions and counterfactuals. 

decisionOS tracks decisions from data, to recommendation, to ultimate outcome and reports that back in the KPIs which your enterprise cares about That way, you can track success by the value-generating decisions you make, rather than technical metrics like model accuracy. 

Buy better outcomes 

According to Forrester, marketplaces for SaaS sourcing help alleviate issues such as SaaS sprawl, excessive cost and security risks “while still supporting business-led purchasing, which contributes to increased business agility, better business outcomes, and improved employee experience.” (Source: The Forrester New Wave™: SaaS Marketplaces, Q2 2020.)

That’s true for causaLens, which has seen customer ROI across a range of use-cases:  

• Working with a marketing team saw a 5% decrease in marketing costs by optimising allocation across campaigns. 
• Working with a European bank saw a return of $12mn from using decisionOS to manage risk. 
• Working with a leading IT manufacturer generated $19mn in savings from optimising inventory. 
• Working with a manufacturer saw a $4mn return from optimising their manufacturing lines. 

With support from the Microsoft cloud, causaLens is making an end-to-end Causal AI platform accessible to Enterprises globally via the Microsoft marketplace. This simplifies the overall process, allowing you to efficiently adopt this state-of-the-art technology while spending smarter and reducing complexity. 

Unlock the value of the cloud 

AI software published on the marketplace forms a powerful mix of innovation, reliability, security and convenience. If you’re looking for solutions to fuel your cloud transformation and grow your business, it’s the place to shop.   

To move at the speed of business today, many companies prefer buying to building cloud apps. This allows you to offload the associated costs and management onto SaaS partners.  You’re able to use private offers to get exactly what you need, including customised terms and conditions, negotiated pricing, prototypes for proof of concept, and tailor-made solutions.  

Better still, transact in a single, accessible place, reducing procurement complexity, saving time and simplifying billing. Apply 100% of the value of eligible purchases to your organisation’s Azure cloud commitment and get faster time-to-value with solutions that work with your current technology. 

All while enjoying the peace of mind that comes from buying and running solutions on a trusted cloud with industry-leading security.    

Go from raw data to decisions  

Ready to upgrade your decision making with Causal AI?  

Visit the Microsoft marketplace to buy decisionOS now, or contact our team at ISVUK@Microsoft.com.

Other blogs in this series

Blog 1: Driving your AI transformation with the Microsoft marketplace 

Blog 2: Safeguarding your business with AI-powered security solutions 

Blog 4: Deliver transformational employee experiences through AI-empowering solutions

About the author

James joined Microsoft 15 years ago and has held leadership positions across the Consumer, Enterprise, and the Partner teams at Microsoft. James is currently the ISV Ecosystem Lead and has a passion for people and technology coming together to drive customer success. James has been at the forefront of Cloud & Digital transformation for the last 10 years launching new business models and driving transformation through the Microsoft Partner ecosystem resulting in and contributing to exciting new revenue streams and significantly accelerated growth for Microsoft and Partners.

The post Optimising business operations through AI-powered solutions  appeared first on Microsoft Industry Blogs - United Kingdom.

The use of automated detection and response systems can help tip the scale in favour of defenders by using risk-based algorithms and anomalous activity detection to flag events that require human expertise to investigate further. This helps security analysts detect patterns and behaviours that are not obvious to the human eye, with more precision and speed than human defenders alone.

The background to “cognitive cyber”

As advances in dynamic and adaptive cyber defence systems become reality, what do organisations need to do to become ready for cognitive cyber, and what exactly is it?

Cognition refers to the mental processes involved in gaining knowledge and comprehension. Cognitive cyber attempts to simulate that process with the application of self-learning algorithms, natural language processing, and big-data mining techniques as applied to the cybersecurity domain. It uses cognitive system overlays to traditional artificial intelligence (AI)/machine learning (ML) models to achieve something greater than the sum of the parts. 

To recap:

• Classic/traditional AI and ML​ detects and classifies, and can work on vast amounts of data for use in real-time applications and automation of capabilities. ​Traditional AI is strong when it comes to looking at a large field of data and finding patterns or continuations (like making recommendations).
• Generative AI (GAI), often powered by generative pre-trained transformers (GPT), effectively understands and creates content. It works on relatively small chunks of data – text, images, sounds, videos. Large language models (LLMs) are a kind of GAI that work on text.​ LLMs are good at understanding language, summarising, and translating concepts, for example from language to code or vice-versa. ​

Clearly, linking these models makes for a much more powerful narrative. And, by using the compute power, scalability, and richness of the cloud, we can build entire systems of intelligence that can reason over vast amounts of information – structured and unstructured.​

Our name for this intelligence-based cognitive capability? Microsoft Copilots. These are experiences that use generative AI to help humans with complex cognitive tasks.

Introducing Microsoft Security Copilot

Built specifically to augment human security expertise, Microsoft Security Copilot is a combination of the most advanced GPT4 model from OpenAI, with a Microsoft expert-driven, security-specific LLM model.

Most LLMs are trained on corpuses of written human language. Security Copilot is trained on security logs, attack telemetry and threat intelligence, the outcome of which is the first AI/ML model trained specifically for security.

But the capability is much more than just the large language model. Built into the product are specific cyber skills and promptbooks informed by our global threat intelligence, which runs on Azure’s hyperscale infrastructure. This means that the models inherit Microsoft’s comprehensive approach to security, compliance, and privacy. When it comes to the data Copilot is reasoning across, your data remains your data.

Security Copilot democratises defender skills by allowing natural language for querying rather than having to learn complex querying languages like Kusto Query Language (KQL). This lowers the barrier to entry for new analysts, which helps address the cybersecurity skills shortage. We’ve launched an Early Access program for qualified candidates to explore the capabilities of Security Copilot. Reach out to your sales representative to get more details.

Use cases for Microsoft Security Copilot

Human ingenuity and expertise will always be an irreplaceable component of defence, so we need technology that can augment these unique capabilities to improve the analyst experience all-up. For this reason, initially we are focusing on security operations centre (SOC) use cases.

The three primary use cases are security posture management, incident response, and security reporting.​

• Security posture management: Security Copilot delivers information on anything that might expose an organisation to a known threat. It then gives prescriptive guidance on how to protect against those potential vulnerabilities.​ A query such as: ‘How can I improve my security posture?’ will return evidence-based recommendations.

• Incident response: Security Copilot can quickly surface an incident, enrich it with context from other data sources, assess its scale and impact, and provide information on what the source might be. Again, it will support the analyst through the response and remediation steps with guided recommendations.

• Security reporting: Security Copilot can deliver customisable reports that are ready to share and easy to consume to keep managers and other stakeholders in the loop. What this means tactically is you can ask Security Copilot in natural language: ‘Summarise this incident in a single PowerPoint slide’, and it will do just that.

Preparing for cognitive cyber defence: 3 steps

In the future, our vision with Security Copilot is to support use cases across security, identity, management, compliance and more, leveraging skillsets across Microsoft and third-party products. In the meantime, and whilst Security Copilot is not yet publicly available, there are things organisations can do to prepare for these cognitive cyber defence capabilities:

Step 1: Secure your identities, especially privileged identities, and SOC members. Attackers will frequently target these individuals to gain access to critical information and systems to elevate the impact of a successful compromise.

Step 2: The age of AI is also referred to as the age of platforms. Integrating your security signals into an observability platform brings huge security gains in terms of visibility and automation. 

Step 3: Initially, Security Copilot is integrated with Microsoft Defender for Endpoint, and for an even better experience, deploy Microsoft Sentinel and Intune. Going forward, Security Copilot will integrate with third-party products.  

Finally, prepare for the risks. As with any new technology, there are both risks and rewards. To help organisations navigate the risk/reward balance, we’ve released guidance, frameworks, and tooling. 

More information, including links to the risk assessment framework, the Counterfit tool and the Adversarial Threat Matrix (MITRE ATLAS) can be found in our Security blog post Best practices for AI security risk management. 

For information on our commitment to build trustworthy and responsible AI, please read Responsible and trusted AI and Building AI responsibly from research to practice.

Cognitive and AIML technologies are here to stay. While they have the power to bring immense potential for improving our defenders’ experience, securing our organisations, and protecting society, we must also be mindful of potential vulnerabilities on an equally large scale and defend against that risk.

Find out more

Introducing Microsoft Security Copilot

Microsoft Security Copilot Early Access Program

News Center: Microsoft brings the power of AI to cyberdefense

Microsoft Security Copilot: Empowering defenders at the speed of AI

About the author

Previously lead investigator for Microsoft’s detection and response team (DART), Lesley Kipling has spent more than 17 years responding to our customers’ largest and most impactful cybersecurity incidents. As Chief Cybersecurity Advisor, she now provides customers, partners and agencies around the globe with deep insights into how and why security incidents happen, how to harden defences and more importantly, how to automate response and contain attacks with the power of the cloud and machine learning. She holds a Master of Science in Forensic Computing from Cranfield University in the United Kingdom.

The post Cyber defence in the age of AI appeared first on Microsoft Industry Blogs - United Kingdom.

Getting started with Azure Machine Learning

Azure Machine Learning (Azure ML) is a cloud-based environment where you can build and manage machine learning models. It’s designed to govern the entire ML life cycle, so you can train and deploy models without focusing on setup. The platform is suitable for any kind of machine learning, from classical to deep learning, to supervised and unsupervised learning.

Azure ML is structured to help teams of data scientists and ML engineers make the most of their existing data processing and model development skills. Whether you prefer Python or R – or have previous experience with other open-source platforms such as PyTorch and TensorFlow – Azure ML is flexible enough to support these platforms and accelerate your work.

With built-in services like Azure ML studio that provide a user-friendly interface, and Automated Machine Learning capabilities that assist you in model selection and training, Azure ML has tools and features to suit every level of experience.

• Learn more about Azure Machine Learning
• Try Azure Machine Learning for free

Kickstart your Azure Machine Learning journey

Whether you’re a developer or simply someone who wants to get a feel for what Azure Machine Learning is all about, there are plenty of learning resources out there.

• Introduction to Azure Machine Learning
• Use automated machine learning in Azure Machine Learning
• Microsoft Azure AI Fundamentals: Explore visual tools for machine learning
• Train a machine learning model with Azure Machine Learning
• Introduction to the Azure Machine Learning SDK
• Work with Data in Azure Machine Learning

You can also find further learning resources on the Azure ML learning resources page.

Try out Azure Machine Learning

One of the best ways to get grips with new tools and software is simply to give it a go. There’s no better way to do this than getting stuck into Azure Machine Learning itself.

Trying out Azure Machine Learning is free, so give it a whirl today.

• Try Azure Machine Learning for free

Learn more

• Why you can’t always rely on AI when designing for accessibility
• How you can get started with scalable AI as an IT Pro
• Azure Mythbusters: The Who’s Who of the Azure AI Platform
• Azure Mythbusters: Building ML Models faster than you think
• Enjoy free technical sessions from Microsoft experts on Learn TV!

The post Getting started with Azure Machine Learning appeared first on Microsoft Industry Blogs - United Kingdom.

We caught up with Tiernan Mines, who spoke to us about the Hello Lamp Post platform, how they’re using Azure to power their natural language processing, and how they’re creating the future of how people will experience place.

Chris: Who are you, who are Hello Lamp Post and what do you do?

Tiernan: I’m Tiernan, I’m the CEO and one of the co-founders of Hello Lamp Post. As CEO, you can imagine I wear a few different hats and that as we evolve and grow as a company, my role morphs and changes.

I like to think of my role as being a servant to our vision and my team – the team do all of the clever work. I’m there to support and make sure everyone’s moving in the same direction together, achieving our goals and doing so in a fulfilling, enjoyable environment.

In terms of my background and how I came about co-founding Hello Lamp Post, it was mostly a venture and commercial-based background. In the early days I had roles in sales, and since then I’ve founded a couple of smaller ventures. I’ve joined other start-ups to grow particular revenue streams or new products, and eventually Hello Lamp Post was born when working with my two co-founders, Ben and Sam.

As for what we do, we’re a software company that makes anything interactive – outside of the home, in the public realm, anywhere in the world. We do that by using people’s devices combined with QR codes, which allows us to have two-way chat interactions between people and a specific hyper-local place, space or object. This could be anything from a park bench to a bus stop or a building. We also bring this online, allowing website and marketing channels to become interactive.

For companies and organisations, Hello Lamp Post can be described as a customer experience tool for places. It gives people easier access to information, be that live bus times at a bus stop, what the council has planned for your neighbourhood, or the history of a statue. This is really important because this two-way chat provides the organisations and companies that are looking after those places or areas a better view of people’s needs and wants at any given time.

C: Where did the idea behind Hello Lamp Post come from?

T: Hello Lamp Post was born out of a couple of frustrations and observations. Perhaps it was triggered by the whole Smart Cities movement a few years ago but we saw that the digital and physical worlds were coming together, in the sense that you were able to go anywhere and expect that there’s gonna be the Internet, be it Wi-Fi or mobile data. That convergence has been happening for years, but we noticed that when we move through or use physical environments, it’s all very analogue.

Whether you’re at a bus stop during your commute, working in an office, or visiting a park or landmark, everything’s still very analogue. It was the observation that you might pass through an area and want information about that place, you have feedback you want to pass on, or you have ideas about those areas – for most people these thoughts are simply forgotten.

On top of this, whenever you’re using or experiencing a place, there’s always a high barrier to entry for information. Obviously we have access to search engines, but the barriers are still high, right? If you want to find out what the council’s doing in your local area, you have to trawl through a number of websites. If you’re looking at a statue, information is limited to a tiny bronze plaque. Or if you have a tonne of questions while finding your way around a hospital, you’re limited to navigating a complex website, trying to understand complex signage or find an available staff member.

On the flip side, the other frustration is that this is a two-way investment. If I want to communicate, I need to know what company or organisation is looking after the place or object in particular.  Again, it’s not an impossible task to figure out, but the barriers to entry are really high and it cuts off a lot of interaction, and misses out a large portion of society.

So why didn’t a digital platform exist that made it easier to find information, in-location? Also, for the companies and organisations looking after those places, why wasn’t there a platform that made it easier for them to understand their audiences? That’s where the idea came from.

C: How did you build the first iteration of Hello Lamp Post, and how has it changed since then?

T: My co-founders and I were talking about these frustrations and observations, and the direction in which physical worlds will change considerably. We knew they were going to become more interactive in one way or another, whether it’s for gaming, as a utility, whatever it might be, and we saw an opportunity to be a part of that.

Then there’s serendipity, and part of this was an award that we ended up winning. Originally, Hello Lamp Post didn’t exist beyond an idea on a bit of paper between us, but we entered into the Playable City Award which is run by Watershed in Bristol. Whoever wins gets to build out the first iteration of their idea.

We started from a user perspective, imagining a digital journal across a city where objects and places become interactive. How people can use those places to leave their memories and ideas, and how they would be shared with other people when they interact with the same objects and places. We entered Hello Lamp Post as an original concept and we ended up winning the inaugural award, which allowed us to build our first iteration.

We eventually deployed in Bristol for eight weeks and it was a considerable success. We proved that people will have conversational chats with inanimate objects, and that became our first jumping off point.

At the time we saw huge potential, but we never expected it to become a tool, a platform, a solution or service. But off the back of that, a number of different cities and organisations around the world got in touch to ask if this was a platform that could be used to engage and communicate in a more automated way.

What started as a one-off concept ended up morphing into a useful and powerful tool, both for us as people and also for decision makers in the public and private sector.

C: In what ways are people currently using Hello Lamp Post?

T: The beauty of what we’ve built is that the interactive layer can be anywhere. There are really no limits on where Hello Lamp Post can be deployed to have two-way exchanges. So we don’t need to be stuck in the age of reading bronze plaques, scrolling through websites, hosting a town-hall meeting or sending someone out with a clipboard to run surveys.

To go deeper on a couple of examples, fire and rescue services are using our platform to allow people to do an automated at-home safety check from their own device, which triages them on whether they’re at risk of fire at the home or not. It allows the fire service more visibility on fire risks, and in the long term it reduces the number of avoidable fires in the home.

We’re also working with hospitals, automating their process of gathering feedback. With the NHS and other healthcare services being extremely strained, things like engaging with patients and getting their feedback fall by the wayside, so we’re helping to make sure that this doesn’t happen.

At the same time, some councils are using Hello Lamp Post for public wellbeing and suicide awareness/prevention – making key locations interactive to give people essential help and information at times of critical need. Again, this is all done through smart two-way interaction.

C: You use Azure for natural language processing – can you tell us more about how you use it?

T: For us, we break natural language processing (NLP) down into three parts: the customer-facing side, the user-facing side, and the internal side that we use. We tend to use a lot of natural language processing for the customer-facing side, with regards to presenting insights back to them.

Public and private sectors like to gather a lot of data, but we’re moving into an age of insights rather than just data. We wanted a way to distil all of the free-text responses and data that’s coming through the platform and present it back as useful insights at the click of a button. To that end, we use a lot of analytics around NLP and other areas that then feed to dashboards that our customers can look into.

From a user perspective, it’s really about using NLP live to assess what a user is saying, and that can be anything including sentiment, query recognition and classification of theme. Our algorithms have to understand what the user is saying and then decide what to do next.

Internally, we use NLP a lot with our analytics. We frequently analyse our databases to spot trends and how we can better utilise the data that we have access to. In the near future, it means better experiences for our users and customers.

Automation is a huge part of why we use NLP. In the early days of Hello Lamp Post we had to do a lot of manual analytics with spreadsheets, pivot tables and the like, so the dawn of natural language processing has really helped to automate a lot of that.

C: Why did you decide to use Azure over other cloud providers?

T: The kicking off point for us using Azure was when we when we got accepted into Microsoft AI for Good program. We were a part of the 2020 cohort, and it became a real eye opener to the Azure platform for us. We became more familiar with the different toolsets, the different infrastructure offerings and it gave us a good opportunity to sandbox a lot of what Azure has to offer. That’s when we realised how seamless it is.

Scalability was also very appealing as our ambitions were always to scale and grow, and we knew that the Azure platform could underpin all of that. It was a combination of being able to seamlessly use various tools that can talk to each other, but also knowing that if we grew rapidly then things wouldn’t fall over.

C: Do you use any open source software? If so, which software?

T: Yes, we do use open source software. We’re currently using Postgres, and some of our front end, externally-facing interfaces use Vue3 and React. On top of this, we also use several component libraries.

C: Back in 2020, during the height of the Covid-19 pandemic, you launched an online version of Hello Lamp Post e.g. Hello Council. Did you foresee yourselves running something like that eventually? Did it affect the direction of the business in any way with what you learned?

T: I think we always envisaged moving in that direction, so the short answer is yes. We always wanted to bring the offering online, and obviously things like engagement, communication and customer experience don’t only exist in the physical world, so really the pandemic just accelerated that move for us.

Interestingly, there was an increased demand for what we do in the physical world at the start of the pandemic because companies weren’t able to have people out and about, that they now needed a contactless experience. Others wanted a more automated way to bring our platform online, think adding QR codes to letters that councils were sending out for example, to make those letters interactive. Councils were one of the types of organisations that pushed us online sooner.

In terms of lasting effects, I wouldn’t say it changed our direction, but we definitely became more informed. One of the key things we learned is that it’s clear that customer experience isn’t one-size-fits-all, it’s multi-faceted and about making it more accessible by lowering the barriers to entry. I think that was the biggest learning for us.

C: What’s next for Hello Lamp Post?

T: Our focus now is really about accelerating growth to help more customers and communities around the world. We’re really focused specifically on growing our customer base in the UK and North America, and we’ve got a team based in the US now which is really exciting, growing that market across public and private sectors.

Without saying too much we’ve got a really exciting product roadmap coming up as well, so we’re really accelerating towards that, but our main focus is just wanting to work with and help more customers and communities around the world.

C: What is Hello Lamp Post’s vision?

T: Ultimately, we want to make everywhere interactive. That might sound lofty, but what’s driving us is making it easier to connect people, information and place. There’s a sweet spot that our platform sits in that no one else does, which is partly why we’re shooting so high.

It does have a purpose, of course – the more people are connected, the better they’re informed about and involved in the places where they live, work and play. Our goal is to deploy in as many places as possible around the world and create the future of how we experience ‘place’. That ultimately feeds into our mission – make places better for people. 

Learn more

• Visit the Hello Lamp Post website
• Building on Microsoft Azure to smartly connect people and places
• Microsoft Azure AI Fundamentals: Explore natural language processing
• Create a Language Understanding solution with Azure Cognitive Services

The post How Hello Lamp Post use Azure to help cities better understand their citizens appeared first on Microsoft Industry Blogs - United Kingdom.

The teams who develop and manage these solutions – developers – are at the heart of the organisation. They’re critical in enabling your organisation to respond to your customer’s needs.

And when it comes to digital innovation, speed is crucial but so is having a structured plan in place. At the same time, innovation is open to everyone. Therefore, organisations need the right tools to create a culture of innovation.

Professional developers can use Visual Studio and GitHub to modernise existing and develop new applications.

You can also empower a new stream of innovation– citizen developers. These employees understand a business process and want to improve on it but might not have the developer expertise. Now with Power Apps, they can use low/no code solutions to build what they need.

Here are six ways to build a customer-centric culture by empowering your developers.

Move to the public cloud

Innovation happens faster in the cloud. Whether you need to modernise existing applications, simplify complex environments or create new apps, you can benefit from the scalability and flexibility of Azure. Developers can build on a secure foundation in any language or foundation, from anywhere.

Simplify complex and distributed environments across multiple clouds and edge environments with Azure Hybrid cloud solutions. Bring Azure management to your entire IT estate and run Azure services anywhere.

As the UK’s leading omnichannel payments business, PayPoint needs to maintain business as usual while managing increasing demand for its services. With Azure, they were able to respond with agility and even develop and deploy new functionality without downtime to customers.

Shorten time-to-market

According to a Gartner survey study, positive customer experiences drive more revenue, higher employee satisfaction and greater customer retention.

Organisations have a strong sense of urgency in going digital. This is driving demand for tools and services that shorten time-to-market and drive those positive customer experiences.

With tools like Azure DevOps and Visual Studio Code, with automation through DevOps Pipelines, GitHub Actions, the ability to streamline business processes with Power Automate and more increases the efficiency of your developer teams. They can then focus on innovating the customer experience.

Reassess investments

Customers increasingly expect products and services that factor in what they care about – be that macro topics like climate impact, or micro impacts such as their experience interacting with your products.

To meet these demands, organisations must find new ways to deliver service at scale. They need to focus on and connect with the customer experience – no matter how many business units, systems, supply chains and processes that customer journey may span. And do this all while reducing costs.

This requires a new way of thinking.

Many organisations are starting by setting a strategic approach and thinking of themselves as a software company first. Then, they’re leveraging digital technology to deliver on their vision.

Solutions built using the Azure platform offer near-instant provisioning of resources. This lowers innovation costs and enables a faster time-to-market. In fact, Forrester found the average cost to develop an application is 74 percent less with Power Apps.

Empower developers

According to McKinsey & Company, organisations with developer velocity experience four to times faster revenue growth, 60 percent higher return to shareholders, and 20 percent higher operating margin.

Unleash the creative energy of developers by leveraging Azure innovation tools. This enables them to build productively, foster secure collaboration, and remove barriers so they can scale faster innovation at lower cost.

Help them build the skills they need to bring ideas to life with certifications and training. Give developers autonomy, decision making and automate back-end processes so they can focus on bringing innovation.

Drive citizen development

Over 86 percent of organisations already struggle to hire developers. In fact, Gartner predicts that by 2023, there will be four times as many end-user or citizen developers, compared to experienced developers in enterprises.

Empower the people closest to the problem to become citizen developers and solve problems themselves. With low/no code solutions like Power Apps anyone, regardless of their technical capability, can work together on the same platform to create solutions with a high level of agility.

Heathrow Airport employees have eliminated 75,000 pages of paperwork and reduced data entry by nearly 1,000 hours through the low-code development of 30 apps, helping the airport reduce its costs.

Infuse intelligence

The applications that both developers and citizen developers are building are powering important customer centric business processes. By applying AI and machine learning, organisations can infuse intelligence with real-time personalisation and serve up customised algorithms.

Alder Hey Children’s Hospital, uses Azure’s integrated platform with AI to build an algorithm that predicts bed space utilisation. The data is then available on Power BI so healthcare employees can quickly and simply understand the insights.

Empower developers to build a customer-centric culture

By unleashing the full potential of developers and citizen developers, organisations will enable growth, solve a wide range of business problems, and drive digital modernisation.

According to McKinsey, organisations with a developer mindset have 4 to 5 times higher revenue growth and 55 percent higher innovation.

Build a growth mindset culture where developers can drive innovation from anywhere, powered by a comprehensive portfolio of technology that complements your business needs.

We are the only company that has that full stack that spans across the breadth of both tech adoption and tech capability to build, and ultimately increase your chance of succeeding.

Find out more

Resources for leaders:

Watch the webinar: Unleash your developers to innovate

Take the Developer Velocity Assessment

Imagine digital innovation that makes a difference

Deliver a seamless experience with real impact

Build a growth mindset

Make app building easier

Resources for developer teams:

Explore the Dev Hub

Watch Microsoft Build on demand

Get certified

About the author

Denise leads the the digital and app innovation team, working directly with customers to uncover new opportunities. She has over 20 years’ experience in transformation leadership and business change delivery. With a proven ability to drive growth, innovation and performance turnaround across complex organisations, she has delivered new cloud services, automated processes and ways of working across the largest banking system in the UK.

The post The developer-customer connection: Why dev-centred cultures are customer-centric cultures appeared first on Microsoft Industry Blogs - United Kingdom.

For 30 years, our research community at Microsoft has worked across disciplines, institutions, and geographies to envision and realise the promise of new technologies for Microsoft and for society. Today, we’re inviting the global science and technology community to continue this exploration—because ensuring that future advancements benefit everyone is up to all of us.

At 9am Tuesday 19th October until the 20th, Microsoft will be hosting numerous talks across a huge variety of tracks. From Responsible AI to Machine Learning and the New Future of Work, there really will be something for everyone. Then on Thursday 21st, the tracks and sessions will shift into various workshops. Be sure to check out the full extent of the talks and workshops on the agenda page.

The Microsoft Research Summit will be streaming virtually across three time zones, so you’ll have the opportunity to hear from science and technology leaders from around the world—people who are driving advances across the sciences and pushing the limits of technology toward achieving a meaningful impact on humanity. Register now to take advantage of the session scheduler and plan out your Microsoft Research Summit experience – we’ll see you there!

Learn more

• Register now on the Microsoft Research Summit website
• Follow Microsoft Research on Twitter for updates and announcements
• Get started with artificial intelligence on Azure

The post Join us on October 19th for the inaugural Microsoft Research Summit! appeared first on Microsoft Industry Blogs - United Kingdom.

Intelligent automation to streamline operations

A McKinsey 2019 study found that 60 percent of all occupations have at least 30 percent of activities that could be automated. Intelligent automation is the combination of three key factors:

Digital Process Automation (DPA): Lets you automate workflows between applications and services, sync files, get notifications, collect data, and perform other common tasks across modern cloud services.

Robotic Process Automation (RPA): Turns manual tasks into automated workflows by recording and playing back human-driven interaction with legacy on-premise software systems. You can program RPA to run unattended, accelerating automation of high-volume, repetitive tasks or automate cumbersome or repetitive processes while still allowing for human intervention.

AI: Integrate AI models into workflows with a low or no-code approach.

Using automation in finance to streamline operations

Together, these three factors combine to build automated workflows across all the apps, services, and on-premise legacy systems at the same time. Bridging DPA, RPA and AI into Power Platform makes it possible to create secure and compliant automation in your construction firm.

What happens when you use automation to take over manual time-consuming processes? You can free your financial team to focus on higher value work.

Automation can streamline operations and processes, such as invoicing. You can also use it to predict or view insights in real-time. You can also connect silos across your whole firm, giving your whole organisation a single source of truth.

Collecting intelligence at a larger scale will lead to better services and streamlined operations. This will lead to greater value driven, and increased agility.

For example, when connecting your finance data to your supply chain you forecast labour and product needs. This helps you make predictive and real-time decisions. As a result, construction projects are delivered quicker. You can also react quicker to external circumstances.

Grow with agility and resilience

Automation is a long-term strategy. You do get quick wins when you streamline operations and workflows – can you imagine how much time I would have saved in my finance role? But when connected with AI, it can help uncover and create new efficiencies in the future.

The important thing to remember about automation is that it amplifies our work. It lets people focus on strategic work. The Total Economic Impact of Power Apps, a commissioned study conducted by Forrester consulting on behalf of Microsoft in March 2020 found that employees were saving almost two hours a week from automation.

Construction firms have a lot of different needs, especially between different teams and on-site frontline workers. By giving everyone the tools to do their jobs smarter and faster, you can be more agile and resilient.

Find out more

Learn more about how we can help your business transform today

Discover how Power Automate can streamline repetitive tasks and paperless processes.

Learn how businesses reduced development costs and increased overall efficiency using Power Automate in this commissioned Total Economic Impact™ study.

Resources to empower your development team

Learn how to implement robotic process automation

Watch Microsoft Tech Days on-demand: An introduction to Power Platform 

About the author

Simon worked in practice for seven years before moving into the commercial sector as Finance Director of a global Industrial Pumps Business, mainly focussed on the oil and construction sectors. Simon then moved into sales of ERP Software. He now has over 25 years’ experience of selling ERP, with over 500 successful implementations – many on a global basis – across multiple sectors. He focussed primarily on construction, professional services, and field service industries, but also assisted many in the distribution, manufacturing, education and non-profit sectors.

Simon has now been at Microsoft for over three years and has brought his considerable ERP experience to become Sales Lead for Dynamics 365 Finance & Supply Chain Management in the built sector.

The post How to use process automation in finance to streamline operations for construction firms appeared first on Microsoft Industry Blogs - United Kingdom.

So what we can do with machine learning? We can first use it to understand an individual’s learning needs. It can then be used to improve the quality, effectiveness, and efficiency of teaching.

Using technology to disrupt education

Teachers often understand the complexity of their student’s parent’s lives (often, they’re in the same boat!). Especially when remote learning and extra support at home can generate real pressures. But parents are always keen to help. And they now have a greater awareness than ever of the array of remote learning options available. Machine learning can help take the complexity out of giving students the extra help they need, coupled with adaptive learning.

What is adaptive learning?

Adaptive learning uses software to change what is presented to a student based on their interactions. To the student, it may seem that they are being presented with content, questions, and hints that are either preordained or random. The reality is that the adaptive learning algorithms are carefully guiding their path. When adaptive learning is blended with effective teaching, students can master and retain skills 1.5 times faster.

Many have looked to build more effective systems, leveraging the power of cloud and the opportunities it gives for data processing and machine learning.

If I were a Hollywood scriptwriter, this is the point for a montage with screens full of numbers crunching away, automating the creation of an education system. Unfortunately, it is not that easy. It also, disappointingly, doesn’t involve a montage filled with techy or 80s music (I’ll let you choose that one).

Back to reality. Here are the four most important things we learnt when we implemented adaptive learning with machine learning:

1. Consider the human side of technology to keep students engaged

We all have different preferences of learning style, such as visual, auditory, reading/writing, and kinaesthetic. A teacher will recognise this and will adapt their approach for individuals as appropriate, particularly where one encounters an area of difficulty. This, however can take a lot of time away from the teacher to deliver these different styles to each student. Machine learning can automatically deliver content to suit the learning preferences for individual students, freeing up time for teachers to spend supporting their students in other ways and answering any questions they might have. It can also help with employing gamification techniques to provide a fun and interactive learning experience for students.

2. Be clear on the outcomes you want to achieve and get your data in order

We wanted to use adaptive learning to help students master and retain skills as efficiently as possible. To help this, we gave our machine learning program a dataset of previous interactions. This enabled us to predicts future events based on that previous data.

The more the program is used by students and the more new data it collects, the smarter your machine learning system will be as it learns to adapt and provide outcomes that are closer to what you set out to achieve at the start.

3. Determine how and when to intervene

Reinforcement is used to ensure that students continue to be exposed to skills that have been mastered until they have demonstrated a solid understanding. To achieve this, there are a number of variables adaptive learning will use. This includes the order that skills are presented, when to progress or regress skills, and when and how to intervene if a student is becoming stuck.

4. Look for opportunities to understand and optimise

Using machine learning optimises the learning path by enabling you to use a far more complex set of variables. This could include the time taken for a student to answer a question, and the geographical location (and therefore school system and learning method taught). When given a wrong answer, machine learning can identify and reinforce the skill that was the source of the misunderstanding.

Doing this will help a student retain and learn skills quickly, as well as ensuring they are getting the right information, at the right time.

Unlock children’s learning potential

Online education tools are a great resource for parents. Machine learning, when applied intelligently to adaptive learning, has the ability to provide highly optimised support for accelerating children’s progress. However, this is not purely a numbers game. The human touch is a critical element in providing true long-term engagement and progression.

When assessing tools available, try and choose those that best support the power of adaptive learning with human intervention.

If you have an idea where you think machine learning may help and want to know more, we would love to spend the day with you discussing the art of the possible.

Find out more

Discover tools and technology for distance learning

Learn how to create and collect data through adaptive assessments 

Explore more about Talk Think Do

Explore Learning

Grow your skills and expertise at the UK Partner Skills Hub

About the author

Matt is CEO of Talk Think Do, a UK based Microsoft Gold Partner specialising in cloud native solutions. Before Talk Think Do, Matt amassed 25 years’ experience in software development, working on high profile projects for world-renowned organisations. Employed first as a developer and then as a solutions architect, Matt’s CV includes work for Barclays, Fitness First and several Formula 1 teams.

Today, his focus is on the direction of Talk Think Do, growing the business by developing strategic partnerships and building the technical capability of the team.

The post 4 steps to enhance children’s education with machine learning appeared first on Microsoft Industry Blogs - United Kingdom.

We set up Agrimetrics to help address challenges in the food system using new technology and data to remedy the situation. We highlight how a range of technologies can tackle complexity in the food system and make it more resilient.

Untangling a complex system

While it’s clear that technology is powerful – the challenge is creating effective business models that support solving these problems.

I get excited about the idea that sharing and connecting data can yield insights that would not be possible without the latest technologies. However, it requires a connection to data that is hard to build in a sustainable way. And quite often, people don’t share data without a value exchange. Also, organisations have to weigh the risk vs. value equation in sharing that data – because we all know that data can be misused.

In my previous role as Director of the Centre for Food Security, one of the emerging themes was the complexity of the food system. The lack of sharing data has made the food system inherently unpredictable and vulnerable.

The food system is dependent on factors beyond human control like the weather. Add on to that the fact that food is at the heart of human existence, and issues in the system can have far reaching consequences. The food system is global. It’s not inconceivable that a drought in one part of the world could impact food shortages elsewhere.

Serendipitously, I came upon the opportunity to bid for funding to create a Centre for Agri-Informatics and Metrics of Sustainable Intensification. I jumped at the chance. A colleague and I started to discuss how we could use data to reduce complexity. In particular, we wanted to tackle the challenge of reconnecting the farmed, natural, and human ecosystems. These have tended to be managed independently of one another.

These are of course one ecosystem. By building close connections between the ecosystem and its digital representation through data, we can fix this disconnect.

Innovating the industry by combining science and technology

Agrimetrics is a company that is truly at the intersection of science and technology companies. This relationship has always existed, but now it is going beyond the core technical aspects into creating something bigger. We will discuss what this means in practice, and some of the technologies that have changed the way we can build on data.

We are essentially the food and farming sector’s Data Marketplace: a place to find, manage and monetise agri-food data. Our mission is to accelerate the sector’s ability to maximise the value of its data. We want to see a sector where the sharing of data powers the next generation of innovation.

Connecting fragmented data

Making the most of the data in agriculture is harder than in many industries because of its fragmentation. At a much more practical level, we are using technology to provide an infrastructure that supports an agri-food data marketplace.

The key requirements for a data marketplace include:

• Interoperability of data
• Connected data
• Data originators need control over their data
• Value needs to be exchanged
• Information needs to be symmetric. Users need to understand the data they are accessing and providers need to know how their data is being used.

All of these requirements bring technical challenges, including the need for detailed permissioning below the level of the data set. The most interesting to me however is interoperability. Many are tempted to think that standardisation is the route to interoperability. However, this imposes rigidity on the data model when there is a range of data in agriculture.

Agricultural data includes numerical data like prices and yields. It also includes things like plant names, which might be in Latin or a local language. There is also the human challenge. Some data-standards that are different in one area compared to another. Persuading one community of users to abandon their cherished standard in favour of another is likely to be problematic.

We are adopting an alternative. By using semantic data models we are providing comprehensive, machine readable descriptions of the entities which the data is intended to represent as well as the relationships between them. The value of this approach is that it begins with a fundamental description of the world which can be made real in many different ways.

The description begins with the general case, for example: a cow produces milk, humans drink milk, and ends at the specific: ermintrude is a cow; ermintrude produced 5000 litres of milk last year; Susan is a human; Susan drank a pint of milk yesterday.

The value of this approach is that it begins with a fundamental description of the world which can be made real in many different ways. For example the cow entity can be specified in any language or it can be made to represent a specific cow by using data. This data can be supplied in any form as the example illustrates with differing units used to measure volumes of milk.

Leveraging machine learning and the power of Azure

To build our data marketplace, we realised we needed a sophisticated and connected dataset. We created a knowledge graph with rich semantics to make data interoperable. A knowledge graph uses machine learning to provide structure and create smart relations throughout the dataset.

Graph databases are quite challenging to make performant and useable. They’re built with SPARQL, which isn’t a friendly language and a small mistake can easily bring down a database. This is why you need to combine a range of technologies to tackle any problems. In our case, we used elastic to allow for rapid querying, SQL to store numerical data, and an API called graphDB to take care of the semantic data.

Azure allows us to scale as our data grows. Most importantly, Azure is built with security-by-design to help us keep the data safe. It even uses machine learning and AI to stay ahead of modern security threats and keep its cybersecurity intelligence up to date

Helping create sustainable food production

The volumes of data that are being created today are transforming the way we do science. With plentiful data, we can have much larger samples to produce more actionable insights for the food system.

As we have highlighted above however, making the most of the data in agriculture is harder than in many industries because of its fragmentation. Therefore, we have taken a more practical approach leveraging the power of Azure and machine learning to provide a robust infrastructure that supports the agri-food data marketplace.

By simplifying complex food systems through the use of the data, analytics, and AI we’re improving resilience and helping solve the global challenge of economically, ethically, and environmentally sustainable food production.

Find out more

Four skills needed to use AI for social good

Accelerate competitive advantage with AI

About the author

Richard Tiffin is Agrimetrics’ Chief Scientific Officer and Professor of Applied Economics at the University of Reading.

Richard read Agriculture at the University of Newcastle and completed a PhD in Agricultural Economics at the University of London. He lectured in Agricultural Economics at both Newcastle and Durham before joining the University of Reading where he was appointed Professor in 2006.

Richard was previously Director of the Centre for Food Security, leading the University of Reading’s strategic research in the area of food security and fostering internal and external collaborations to meet the multidisciplinary food security agenda. His research, which is focused on diet and health policy, has examined the impacts of alternative food policies on land use in the UK and the impacts of both a soft drink tax and a ‘fat tax’ on health in the UK.

Richard’s research group is currently developing an empirical framework to better understand the cognitive underpinnings of dietary choice.

Kate is the Head of Azure Cloud Solution Architecture for Media, Telco and Professional Services at Microsoft UK, working with customers to architect end to end solutions, using Microsoft cloud technologies, with an emphasis on creating solutions that leverage data by using AI.

A behavioural neurobiologist by training, she is passionate about the intersection between technology and business, and how new technologies can shape organisations as they evolve.

In her earlier role at Microsoft, she led the Data and AI Cloud Solution Architecture team for Financial Services. Under her leadership, the team helped organisations shape their data strategies in a scalable and responsible way.

Prior to Microsoft, Kate worked at a start-up that used Big Data to predict commodity flows for Financial Services Institutions, focussing on data fusion, macro-economics, and behavioural analysis. She also holds an MSc in Molecular Biology from Bar Ilan University and a MBA from Tel Aviv University.

The post Interconnected data for an interconnected planet appeared first on Microsoft Industry Blogs - United Kingdom.

Here in the UK, our AI for Good accelerator programme is being run in London by Microsoft and Social Tech Trust. It will support purpose-driven ventures working on solutions to help make a positive impact on the world and our community. The four-month accelerator will give the companies a curriculum jointly designed by Microsoft for Startups, the Social Tech Trust and the Microsoft UK AI team, of which I’m part of.

In 2019 we saw 11 great cohort members graduate, and this year we have welcomed another 12 which we are looking forward to taking through the process.

They’ll get access to workshops and data and AI hacks that will help develop their products and bring them to the market. Also, they will hear from inspiring speakers and take part in Q&As.

When I think about the kind of skills can equip businesses with to develop AI for good capabilities, I wanted to ensure these were relevant across sectors and business sizes. I also want it to be easier for all types of organisations to adopt responsible technology initiatives.

This year there is a focus on execution. We aim for every one of the 12 start-ups to have integrated AI. Moreover, this integration will be done in a very meaningful and responsible way.

“To thrive in their digital transformation, institutions and organisations have to embrace tech intensity. Not only do they have to become fast adopters of best in class technology, at the same time they have to build their own digital capability. I am proud to see the concept of tech intensity come to life through in this year’s AI for Good cohort. From aiming to empower every person to participate in the digital economy through accessibility, supporting humanitarian action and preserving as well as enriching cultural heritage, or solving the some of the world’s biggest health and sustainability challenges; we are proud to support and foster these bold ambitions and make a difference to the world we live in, today.”

Derrick McCourt, General Manager, Customer Success Unit Microsoft UK

1.    Get clear on the fundamentals

To help our cohort members infuse their solutions with AI, it’s necessary to build a baseline level of technical knowledge across the group. Our curriculum trace’s official certification paths that are relevant to most of the cohort. These are also available for anyone interested in learning more about AI and its integration with modern AI-infused applications.

• Designing and Implementing an Azure AI Solution
• Designing and Implementing a Data Science Solution on Azure

Throughout this curriculum, the technical members of the cohort will be able to explore AI solution development with data science services in Azure. They will also publish machine learning experiments with Microsoft Azure Machine Learning services.

The more advanced part of the curriculum is split into two parts:

1. The fundamentals covering AI foundation, operationalising AI, and responsible AI
2. Deeper dives on specific use-case with individual cohort members, including hands-on workshops.

By offering a mix of foundational and more advanced modules, the course content stays relevant to both technical and non-technical founders.

2.    Move from theory to operational reality

We are seeing AI solutions across all verticals. However, one of the challenges we see when working with organisations of all sizes is their ability to operationalise AI. At Microsoft, we have a mature and battle-tested way of doing this – we call this MLOps, this is built into our machine learning services such as AutomatedML. Customers who prefer to use Python can opt to use our MLOps for Python.

During this process the cohort will learn about AI and infusing it into their applications. They will also have hands-on experiences to operationalise the entire process end to end.

During the foundational part of the curriculum, we will also cover the following courses:

• Evaluate text with Azure Cognitive Language Services
• Process and translate speech with Azure Cognitive Speech Services
• Build AI solutions with Azure Machine Learning service
• Process and classify images with the Azure Cognitive Vision Services
• Create Intelligent Bots with the Azure Bot Service

[msce_cta layout=”image_right” align=”right” linktype=”blue” imageurl=”http://approjects.co.za/?big=en-gb/industry/blog/wp-content/uploads/sites/22/2019/02/MS_AI_Development_007.jpg” linkurl=”http://approjects.co.za/?big=en-gb/industry/blog/cross-industry/2018/12/04/how-to-implement-an-ethical-framework-in-ai/” linkscreenreadertext=”How to build an ethical framework for your AI applications ” linktext=”Build an ethical framework for your AI apps” imageid=”8266″ ][/msce_cta]

3.    Define what responsible AI looks like to your business

A huge part of our curriculum is the Microsoft Framework for Responsible AI, where they will learn the ethical implications of AI in business. We will share our guidelines to develop a governance model with resources, best practices and tools. As useful as AI is, we must be realistic about the challenges that AI will also raise.

As our President Brad Smith sets out in our eBook The Future Computed; “We don’t believe that we can afford to look at this AI future with uncritical eyes.”

We believe that the development and deployment of AI must be guided by the creation of an ethical framework. Four core principles of fairness, reliability and safety, privacy and security, and inclusiveness. These are underpinned by two foundational principles of transparency and accountability.

Discover how you can be intentional when building AI solutions to focus on the ethical aspects, and how we can build a framework around it:

4.    Deep dive on specific AI use cases

At Microsoft we believe in tech intensity for our customers and partners, enabling them to “become technology organisations and, in the process, placing themselves at the forefront of technology innovation in their industries,” according to Deb Cupp, Microsoft CVP of Worldwide Enterprise and Commercial.

Getting your hands dirty is the best way to achieve tech intensity. At Microsoft, we run software engineering hacks with developers, DevOps engineers and architects to simplify complex projects are part of our culture – so much so that we have formalised a process of doing this with customers though our OpenHacks on Azure.

From the third workshop onwards, the cohort will be going deeper on their specific use cases. They will ensure their applications are integrated with Azure ML and AI in a meaningful way. By the time they graduate, AI will be infused in their application to drive the right impact.

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We’ll also continue to share what we’re learning in our own AI journey. We recognise that we don’t have all the answers. But, we hope we can provide some useful perspectives though our journey with the AI for Good cohort in our shared mission to make the world a better place for humanity and generations to come.

Find out more

Register for the Azure Fundamentals training day

AI for Good Accelerator Programme

[msce_cta layout=”image_center” align=”center” linktype=”blue” imageurl=”http://approjects.co.za/?big=en-gb/industry/blog/wp-content/uploads/2019/10/MS_AI_Agriculture_DataVis_002.jpg” linkurl=”https://info.microsoft.com/UK-DIGTRNS-CNTNT-FY20-10Oct-07-AcceleratingcompetitiveadvantagewithAI-AID-3001579-SRGCM3020_01Registration-ForminBody.html” linkscreenreadertext=”How to accelerate your competitive advantage with AI” linktext=”Accelerate your competitive advantage with AI” imageid=”17037″ ][/msce_cta]

About the author

Pratim is Head of Solutions Architecture, where he runs a team focused on Data & AI for the Customer Success Unit. Prior to that he was at AWS, as a Specialist SA for Big Data & Analytics, where he advised customers on big data architecture, migration of big data workloads to the cloud, and implementing best practices and guidelines for analytics. Pratim is particularly interested in operational excellence for petabyte to exabyte scale operations, and design patterns covering “good” data architecture including governance, catalogue, and lineage. He’s also passionate about advanced analytics, planet scale NoSQL database like Cosmos DB, and using the right mix of technology, business, and pragmatism to ultimately make customers successful.

The post 4 skills organisations can embrace to use AI for social good appeared first on Microsoft Industry Blogs - United Kingdom.