When University of Toronto astronomer Bryan Gaensler looks up at the night sky, he doesnt just see stars he sees data. Big data.

So big, in fact, that his current research tracking the baffling fast radio bursts (FRBs) that bombard Earth from across the universe requires the capture of more data per second than all of Canadas internet traffic.

This is probably the most exciting thing in astronomy right now, and its a complete mystery, says Gaensler, director of U of Ts Dunlap Institute for Astronomy & Astrophysics and Canada Research Chair in Radio Astronomy. Randomly, maybe once a minute, theres this incredibly bright flash of radio waves like a one-millisecond burst of static from random directions all over the sky.

We now know that theyre from very large distances, up to billions of light-years, so they must be incredibly powerful to be able to be seen this far away.

U of T is a world leader in finding FRBs, using the multi-university CHIME radio telescope in British Columbias Okanagan region and a U of T supercomputer. Yet, despite the impressive technology, many daunting challenges remain.

Its a massive computational and processing problem that is holding us back, he says. We are recording more than the entire internet of Canada, every day, every second. And because theres no hard drive big enough or fast enough to actually save that data, we end up throwing most of it away. We would obviously like to better handle the data, so that needs better equipment and better algorithms and just better ways of thinking about the data.

With the creation of U of Ts Data Sciences Institute (DSI), Gaensler and his colleagues now have a new place to turn to for help. The institute, which is holding a launch event tomorrow,is designed to help the universitys wealth of academic experts in a variety of disciplines team up with statisticians, computer scientists, data engineers and other digital experts to create powerful research results that can solve a wide range of problems from shedding light on interstellar mysteries to finding life-saving genetic therapies.

The way forward is to bring together new teams of astronomers, computer scientists, artificial intelligence experts and statisticians who can come up with fresh approaches optimized to answer specific scientific questions that we currently dont know how to address, Gaensler says.

The Data Sciences Institute is just one of nearly two dozen Institutional Strategic Initiatives (ISI) launched by U of T to address complex, real-world challenges that cut across fields of expertise. Each initiative brings together a flexible, multidisciplinary team of researchers, students and partners from industry, government and the community to take on a grand challenge.

Were bringing together individuals at the intersection of traditional disciplinary fields and computational and data sciences, says Lisa Strug, director of the Data Sciences Institute and a professor in the departments of statistical sciences and computer science in the Faculty of Arts & Science, and a senior scientist at the Hospital for Sick Children research institute.

She notes that U of T boasts world-leading experts in fields such as medicine, health, social sciences, astrophysics and the arts, and some of the top departments in the world in the cognate areas of data science like statistics, mathematics, computer science and engineering.

Data science techniques can be brought to bear on a near-infinite variety of academic questions from climate change to transportation, planning to art history. In literature, Strug says, many works from previous centuries are now being digitized, allowing data-based analysis right down to, say, sentence structure.

New fields of data science are emerging every day, says Strug, who oversees data-intensive genomics research in complex diseases such as cystic fibrosis that has led to the promise of new drugs to treat the debilitating lung disease. We have so much computational disciplinary strength we can leverage to define and advance these new fields.

We want to make sure that faculty have access to the cutting-edge tools and methodology that enable them to push the frontiers of their field forward. They may be answering questions they wouldnt have been able to ask before, without that data and without those tools.

A key function of the DSI is the creation and funding of Collaborative Research Teams (CRTs) of professors and students from a variety of disciplines who can work together on important projects with stable support.

Gaensler, who already has statisticians on his team, says hes looking to the CRTs to greatly expand the scope of his work.

We have just done the low-hanging fruit, he says. There are many deeper problems that we havent even started on.

Similarly, Laura Rosella, an associate professor at the Dalla Lana School of Public Health,says the collaborative teams will be a major asset for the university.

Were going to dedicate funding to these multi-disciplinary trainees and post-docs so we can start building a critical mass of people that can actually translate between these disciplines, she says. To solve problems, you need this connecting expertise.

Rosella played a key role in how Ontario dealt with COVID-19 in the early part of 2021. By analyzing anonymous cellphone data along with health information, she and her interdisciplinary team were able to see where people were moving and congregating, and then predict in advance likely clusters of the disease that would appear up to two weeks later. Her work helped support the provinces highly successful strategy of targeting so-called hotspots.

Weve been able to work with diverse data sources in order to generate insights that are used forhigh-level pandemic preparedness and planning, in ways that werent possible before, says Rosella, who sits on Ontarios COVID-19 Modelling Consensus Table. And weve also brought in new angles to the data around the social determinants of health that have shone a light on the policy measures that are needed to truly address disparities in COVID rates.

Rosellas population risk tools also include one for diabetes, which health systems can use to estimate the future burden of the disease and guide future planning. This includes inputs about the built environment. For example, if people can walk to a new transit stop, Rosella says, the increased exercise may have an impact on diabetes or other diseases. Potentially, even satellite imaging data could be brought into the prediction mix, she says.

In addition to advancing research in a given field, the Data Sciences Institute is also seeking to advance equity.

That includes tackling societal inequalities uncovered by data research including how socio-economic factors can determine who is more likely to get COVID-19 and the way the research itself is being conducted.

For example, Strug says most genomics studies have focused on participants of European origin, even though the genetic risk factors for various diseases can differ between different ethnicities.

We must make sure we develop and implement the models, tools and research designs and bring diverse sources of data together to ensure our understanding of disease risk is applicable to all, Strug says.

Many algorithms, or the data they use to make predictions, contain unconscious bias that may skew results which is why Strug says transparency is vital both to support equity and to ensure studies can be reproduced properly.

Gaensler says its critical to ensure diversity among researchers, too.

My department looks very different from the faces that I see on the subway, he says. Its not a random sampling of Canadian society its very male, white and old, and thats a problem we need to work on.

Strug hopes the Data Sciences Institute will ultimately become a nucleus for researchers across the university and beyond.

Theres never been one entrance to the university to guide people, so its so important for us to be that front door, she says.

We will make every effort to stay abreast of the different fantastic things that are happening in data sciences and be able to direct people to the right place, as well as provide an inclusive, welcoming and inspiring academic home.

Link:

Groundbreakers: U of T's Data Sciences Institute to help researchers find answers to their biggest questions - News@UofT

The Transformational Tech series highlights Cisco's nonprofit grant recipient that uses technology to help transform the lives of individuals and communities.

Artificial Intelligence (AI) and Machine Learning (ML) are utilized in many different industries. AI and ML create more efficient virtual healthcare visits and more intuitive online education platforms. They enhance agriculture through IoT devices to monitor soil health, and devise new ways for people to access banking and other financial services.

This type of technology can also be used to improve services that nonprofits provide to local communities. At Cisco, we have a proven track record of supporting nonprofits through our strategic social impact grants along with a strong culture of giving back. Cisco's AI for Good program brings these values together by connecting Cisco data science talent to nonprofits that do not have the resources to use AI/ML to meet their goals.

Cisco AI product manager and former data scientist Arya Taylor leads the AI for Good program. Arya shared, 'AI for Good is specifically dedicated to the data science community at Cisco. We heard that a lot of data scientists want to apply their skills to a problem for good.'

The AI for Good team constantly works to grow its network of nonprofit partners by engaging with the team that manages Cisco's social impact grants and by reaching out to nonprofits directly. One of the organizations that AI for Good volunteers support is Cisco nonprofit partner Replate. Based out of Oakland, California, Replate reduces food waste through a digital platform that makes it easy for companies to schedule on-demand pickups for their surplus food. Replate's food rescuers bring donated food to nonprofit partners who distribute it to people of all ages and backgrounds who are experiencing food insecurity.

Cisco data scientists use ML to forecast food supply and optimize Replate's operations

Cisco's AI for Good team spent six months working with Replate to develop a model that can forecast food supply to maximize food recovery and optimize their operations. Replate's staff met with Cisco's AI for Good team via WebEx to share more about their method of food recovery. Cisco data scientists first assessed the scope of Replate's needs and learned how they could best apply their skills in ML to make an impact.

This Cisco AI for Good project was led by data scientist Aarthi Janakiraman, who also served as cause champion-which means she led the project from start to finish to ensure the project's success. Other members of the project included data scientist Idris Kuti and ML operations expert David Meyer. The team looked at how Cisco's machine learning models would allow Replate to predict surplus food supply within their donor network.

Because Replate offers a variety of donor plans to their partners, it can be challenging to calculate availability and capacity. As a result, Cisco's data scientists developed an ML model that could predict the total pounds of food each donor would contribute on any given day. This more accurate prediction helps Replate's food rescuers, who deliver the food, as well as the nonprofit organizations that rely on meal delivery.

'Before our project started,' Arya explained, 'Replate was using a rules-based model with different thresholds that would determine the estimated amount. But there's no single threshold in machine learning that you can apply to every single donor; it just becomes more personalized to that donor and evolves as more data is collected. So, it works more like our brain, rather than a static generalization.'

Aarthi gave an example: 'Let's say there is a donor for Replate, and they tell us that next Friday they will be able to provide 60 trays of food. This number is often a skewed estimate-donations are typically from grocery stores, corporate cafeterias, or farmer's markets, who may not be able to provide an exact prediction due to the variability of consumption. Our model will take in different information about the donor and estimate a more accurate donation amount in pounds. That estimation will go into Replate's algorithm and match the food rescue task to the correct driver.'

By incorporating machine learning models, Replate can also predict donation volume for existing and new partners. The volume of a new donor's first pickup will be predicted based on data from donors in similar regions or industries. 'Such forecasting will make a significant difference in our operations and allow us to better fulfill our mission,' said Mehran Navabi, senior data scientist at Replate. 'Replate will implement these models into our codebase and integrate them within our existing routing algorithm. The algorithms will coalesce to automate driver-dispatches for each donor's pickup.'

Cisco and Replate: Working together to create lasting change

Replate's team met with Cisco data scientists for biweekly progress reports throughout the project lifecycle and discussed how they could advance their platform's technological capacities. The models that the AI for Good team created will enable smarter dispatching, which will allow a greater volume of food to be recovered and delivered to communities in need.

According to Mehran, one challenge for Replate is meeting the different needs and expectations of their nonprofit partners who serve diverse populations with varying capacities for food storage and meal distribution. Having a model to forecast food supply can reduce waste and help Replate connect food delivery tasks to the correct drivers to ensure as much food as possible will be given to those in need. The project may even increase the amount of surplus food that can be recovered by giving Replate the information needed to make smarter, predictive dispatching decisions.

Now, Cisco's AI for Good team is handing over the project to Replate and will leave them with a maintenance plan which will allow them to retrain the model on Google Cloud Platform. They also built out a service that will track the model's accuracy, so any adjustments can be made as time goes on.

'Working with Cisco's AI for Good team was incredible,' said Mehran. 'Their team was professional and knowledgeable. And overall, their communication was excellent. The partnership enabled Replate to build a fruitful and beneficial connection with the Cisco team and foster new approaches to the way we collect and interpret data.'

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Cisco : data scientists work with nonprofit partner Replate to improve food recovery and delivery to communities in need - Marketscreener.com

Alexander Stokes

Teaching critical data science skills to a broad group of students is the focus of a University of Hawaii project that just received a $300,000 grant from the National Science Foundation (NSF). The John A. Burns School of Medicine (JABSOM) and the Hawaii Data Science Institute (HIDSI) announced the award for the two-year project entitled JADE-Justice-oriented Approaches to Data Science Education.

Data science skills are going to be critical for the jobs of the future. Whether that job is in healthcare, in finance or fighting climate change, data science will be a component of day-to-day employment, in the same way that word processing and spreadsheets became essential 30 years ago, said Principal Investigator Alexander Stokes, JABSOM assistant professor of cell and molecular biology and affiliated faculty with HIDSI. This award focuses on developing these skills in the widest possible group of students, especially those who are not in traditional computer science undergraduate or graduate programs. This NSF-funded research will look at new teaching methods to engage a wide cross-section of students in data science training and research. We want to enrich their undergraduate or graduate experience, and arm them with skills and experiences that give them a competitive edge in tomorrows job market.

HIDSI Co-Director Gwen Jacobs said, We are celebrating this award to Dr. Stokes, who is an excellent example of our virtual institute model at HIDSI, where we invite faculty from a broad range of scientific domains across UH to enrich their research and teaching with data science approaches. As a HIDSI member, Alex started using advanced data science approaches to identify new or understudied therapeutic targets for heart and other diseases and then started exploring the integration of data science and analytics into teaching. This is a highly competitive award that reflects NSF investment both in a research program and an individual who is a future leader in STEM education and research.

The JADE award will support research on data science pedagogy and directly links to Gov. David Iges Digital Economy vision and the NSF 10 Big Ideas focus area of Harnessing the Data Revolution to strengthen science, technology and the economy.

A digital economy recognizes that data are everywhere, Stokes said. As a university, and a state, we need to make sure that the analysts, engineers, physicians, nurses, entrepreneurs, climate scientists, journalists, etc., that we are training, have the skills to enhance their field through data-driven decision making.

Camaron Miyamoto, director of the LGBTQ+ Center at UH Mnoa, who supported the grants design and submission, said the award is also fundamentally about equity.

Alex is looking at inclusive pedagogy in data science, not only for students outside traditional computer sciences, but importantly asking how we reach and include groups who have been historically excluded and marginalized in STEM. Miyamoto said. Alex believes that focusing students on social justice issues in their research projects and involving them in wrangling data that can be used to effect social change, will resonate with under-represented students including BIPOC (Black, Indigenous, People of Color) and LGBTQIA+ (lesbian, gay, bisexual, transgender, queer/questioning ones sexual or gender identity, intersex and asexual/aromantic/agender) participants.

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$300K to teach data science for the jobs of the future | University of Hawaii System News - UH System Current News

If someone were to describe a person of colour as an animal, their comments would be rightly called out as racist. When artificial intelligence does the same thing, however, the creators of that AI are careful to avoid using the r word.

Earlier this month, a video on Facebook featuring a number of black men ended with a prompt asking the viewer if they wanted to keep seeing videos about Primates. Facebooks subsequent apology described the caption as an error which was unacceptable.

An ever-growing catalogue of algorithmic bias against people of colour is referred to by the offending companies using increasingly familiar language: problematic, unfair, a glitch or an oversight.

Campaigners are now pressing for more acknowledgement by these businesses that the AI systems they have built and that have a growing impact on our lives may be inherently racist.

This animalisation of racialised people has been going on since at least 2015, from a great many companies, including Google, Apple and now Facebook, says Nicolas Kayser-Bril, a data journalist working for advocacy organisation AlgorithmWatch.

The infamous incident in 2015, in which two people of colour were labelled by Google Photos as gorillas, caused an outcry, but Kayser-Bril is scathing about the lack of action.

Google simply removed the labels that showed up in the news story, he says. It's fair to say that there is no evidence that these companies are working towards solving the racism of their tools.

To remove systemic racism would necessitate huge work on the part of many institutions in society, including regulators and governments.

Nicolas Kayser-Bril, data journalist

The bias demonstrated by algorithms extends far beyond the mislabelling of digital photos. Tay, a chatbot created by Microsoft in 2016, was using racist language within hours of its launch. The same year, a misconceived AI beauty contest consistently rated white people more attractive than people of colour.

Facial-recognition software has been shown to perform significantly better on white people than black, leaving people of colour susceptible to wrongful arrest when such systems are used by police.

AI has also been shown to introduce levels of prejudice and bias into social media, online gaming and even government policy and yet the subsequent apologies apportion the blame to the AI itself, rather like a parent trying to explain the actions of a naughty child.

But as campaigners point out, AI only has one teacher: human beings. We might think that AI is neutral, a useful way of removing bias from human decision making, but it appears to be imbued with all the inequalities inherent in society.

"Data is a reflection of our history, says computer scientist Joy Buolamwini in the Netflix documentary Coded Bias. The past dwells within our algorithms.

In a striking scene from the documentary, Buolamwini, a woman of colour, uses a facial recognition system that reports back no face detected. When she puts on a white mask, she passes the test immediately. The reason: the algorithm making the decision has been trained on overwhelmingly white data sets.

For all the efforts being made around the world to forge a more inclusive society, AI only has the past to learn from. If you feed a system data from the past, it's going to replicate and amplify whatever bias is present, says Kayser-Bril. AI, by construction, is never going to be progressive.

Data can end up creating feedback loops and self-fulfilling prophecies. In the US, police using predictive software direct greater surveillance of black neighbourhoods because that is where existing systems are prioritising. Prospective employers and credit agencies using biased systems will end up making unfair decisions, and those at the sharp end will never know that a computer was responsible.

This opacity, according to Kayser-Bril, is both concerning and unsurprising. We have no idea of how widespread the problem is because there is no way to systematically audit the system, he says. It's opaque but I would argue that it's not really a problem for these private companies. Their job is not to be transparent and to do good.

Some companies certainly appear to be acting positively. In 2020, Facebook promised to build products to advance racial justice this includes our work to amplify black voices".

Every apology from Silicon Valley is accompanied by a commitment to work on the problem. But a UN report published at the beginning of this year was clear where the fault lies.

AI tools are mainly designed by developers in the West," it said. "In fact, these developers are overwhelmingly white men, who also account for the vast majority of authors on AI topics.

The report went on to call for more diversity in the field of data science.

People working in the industry may bristle at accusations of racism, but as Ruha Benjamin explains in her book Race After Technology, it is possible to perpetuate racist systems without having any ill intent.

No malice needed, no N-word required, just lack of concern for how the past shapes the present, she writes.

But with AI systems having been painstakingly built and taught from the ground up over the past few years, what chance is there of reversing the damage?

The benchmarks that these systems use have only very recently started to take into account systemic bias, says Kayser-Bril. "To remove systemic racism would necessitate huge work on the part of many institutions in society, including regulators and governments.

This uphill struggle was eloquently expressed by Canadian computer scientist Deborah Raji, writing for the MIT Technology Review.

The lies embedded in our data are not much different from any other lie white supremacy has told, she says. They will thus require just as much energy and investment to counteract.

Updated: September 13th 2021, 4:35 AM

Common OCD symptomsand how they manifest

Checking: the obsession or thoughts focus on some harm coming from things not being as they should, which usually centre around the theme of safety. For example, the obsession is the building will burn down, therefore the compulsion is checking that the oven is switched off.

Contamination: the obsession is focused on the presence of germs, dirt or harmful bacteria and how this will impact the person and/or their loved ones. For example, the obsession is the floor is dirty; me and my family will get sick and die, the compulsion is repetitive cleaning.

Orderliness: the obsession is a fear of sitting with uncomfortable feelings, or to prevent harm coming to oneself or others. Objectively there appears to be no logical link between the obsession and compulsion. For example, I wont feel right if the jars arent lined up or harm will come to my family if I dont line up all the jars, so the compulsion is therefore lining up the jars.

Intrusive thoughts: the intrusive thought is usually highly distressing and repetitive. Common examples may include thoughts of perpetrating violence towards others, harming others, or questions over ones character or deeds, usually in conflict with the persons true values. An example would be: I think I might hurt my family, which in turn leads to the compulsion of avoiding social gatherings.

Hoarding: the intrusive thought is the overvaluing of objects or possessions, while the compulsion is stashing or hoarding these items and refusing to let them go. For example, this newspaper may come in useful one day, therefore, the compulsion is hoarding newspapers instead of discarding them the next day.

Source: Dr Robert Chandler, clinical psychologist at Lighthouse Arabia

Common OCD symptomsand how they manifest

Checking: the obsession or thoughts focus on some harm coming from things not being as they should, which usually centre around the theme of safety. For example, the obsession is the building will burn down, therefore the compulsion is checking that the oven is switched off.

Contamination: the obsession is focused on the presence of germs, dirt or harmful bacteria and how this will impact the person and/or their loved ones. For example, the obsession is the floor is dirty; me and my family will get sick and die, the compulsion is repetitive cleaning.

Orderliness: the obsession is a fear of sitting with uncomfortable feelings, or to prevent harm coming to oneself or others. Objectively there appears to be no logical link between the obsession and compulsion. For example, I wont feel right if the jars arent lined up or harm will come to my family if I dont line up all the jars, so the compulsion is therefore lining up the jars.

Intrusive thoughts: the intrusive thought is usually highly distressing and repetitive. Common examples may include thoughts of perpetrating violence towards others, harming others, or questions over ones character or deeds, usually in conflict with the persons true values. An example would be: I think I might hurt my family, which in turn leads to the compulsion of avoiding social gatherings.

Hoarding: the intrusive thought is the overvaluing of objects or possessions, while the compulsion is stashing or hoarding these items and refusing to let them go. For example, this newspaper may come in useful one day, therefore, the compulsion is hoarding newspapers instead of discarding them the next day.

Source: Dr Robert Chandler, clinical psychologist at Lighthouse Arabia

Common OCD symptomsand how they manifest

Checking: the obsession or thoughts focus on some harm coming from things not being as they should, which usually centre around the theme of safety. For example, the obsession is the building will burn down, therefore the compulsion is checking that the oven is switched off.

Contamination: the obsession is focused on the presence of germs, dirt or harmful bacteria and how this will impact the person and/or their loved ones. For example, the obsession is the floor is dirty; me and my family will get sick and die, the compulsion is repetitive cleaning.

Orderliness: the obsession is a fear of sitting with uncomfortable feelings, or to prevent harm coming to oneself or others. Objectively there appears to be no logical link between the obsession and compulsion. For example, I wont feel right if the jars arent lined up or harm will come to my family if I dont line up all the jars, so the compulsion is therefore lining up the jars.

Intrusive thoughts: the intrusive thought is usually highly distressing and repetitive. Common examples may include thoughts of perpetrating violence towards others, harming others, or questions over ones character or deeds, usually in conflict with the persons true values. An example would be: I think I might hurt my family, which in turn leads to the compulsion of avoiding social gatherings.

Hoarding: the intrusive thought is the overvaluing of objects or possessions, while the compulsion is stashing or hoarding these items and refusing to let them go. For example, this newspaper may come in useful one day, therefore, the compulsion is hoarding newspapers instead of discarding them the next day.

Source: Dr Robert Chandler, clinical psychologist at Lighthouse Arabia

Common OCD symptomsand how they manifest

Checking: the obsession or thoughts focus on some harm coming from things not being as they should, which usually centre around the theme of safety. For example, the obsession is the building will burn down, therefore the compulsion is checking that the oven is switched off.

Contamination: the obsession is focused on the presence of germs, dirt or harmful bacteria and how this will impact the person and/or their loved ones. For example, the obsession is the floor is dirty; me and my family will get sick and die, the compulsion is repetitive cleaning.

Orderliness: the obsession is a fear of sitting with uncomfortable feelings, or to prevent harm coming to oneself or others. Objectively there appears to be no logical link between the obsession and compulsion. For example, I wont feel right if the jars arent lined up or harm will come to my family if I dont line up all the jars, so the compulsion is therefore lining up the jars.

Intrusive thoughts: the intrusive thought is usually highly distressing and repetitive. Common examples may include thoughts of perpetrating violence towards others, harming others, or questions over ones character or deeds, usually in conflict with the persons true values. An example would be: I think I might hurt my family, which in turn leads to the compulsion of avoiding social gatherings.

Hoarding: the intrusive thought is the overvaluing of objects or possessions, while the compulsion is stashing or hoarding these items and refusing to let them go. For example, this newspaper may come in useful one day, therefore, the compulsion is hoarding newspapers instead of discarding them the next day.

Source: Dr Robert Chandler, clinical psychologist at Lighthouse Arabia

Common OCD symptomsand how they manifest

Checking: the obsession or thoughts focus on some harm coming from things not being as they should, which usually centre around the theme of safety. For example, the obsession is the building will burn down, therefore the compulsion is checking that the oven is switched off.

Contamination: the obsession is focused on the presence of germs, dirt or harmful bacteria and how this will impact the person and/or their loved ones. For example, the obsession is the floor is dirty; me and my family will get sick and die, the compulsion is repetitive cleaning.

Orderliness: the obsession is a fear of sitting with uncomfortable feelings, or to prevent harm coming to oneself or others. Objectively there appears to be no logical link between the obsession and compulsion. For example, I wont feel right if the jars arent lined up or harm will come to my family if I dont line up all the jars, so the compulsion is therefore lining up the jars.

Intrusive thoughts: the intrusive thought is usually highly distressing and repetitive. Common examples may include thoughts of perpetrating violence towards others, harming others, or questions over ones character or deeds, usually in conflict with the persons true values. An example would be: I think I might hurt my family, which in turn leads to the compulsion of avoiding social gatherings.

Hoarding: the intrusive thought is the overvaluing of objects or possessions, while the compulsion is stashing or hoarding these items and refusing to let them go. For example, this newspaper may come in useful one day, therefore, the compulsion is hoarding newspapers instead of discarding them the next day.

Source: Dr Robert Chandler, clinical psychologist at Lighthouse Arabia

Common OCD symptomsand how they manifest

Checking: the obsession or thoughts focus on some harm coming from things not being as they should, which usually centre around the theme of safety. For example, the obsession is the building will burn down, therefore the compulsion is checking that the oven is switched off.

Contamination: the obsession is focused on the presence of germs, dirt or harmful bacteria and how this will impact the person and/or their loved ones. For example, the obsession is the floor is dirty; me and my family will get sick and die, the compulsion is repetitive cleaning.

Orderliness: the obsession is a fear of sitting with uncomfortable feelings, or to prevent harm coming to oneself or others. Objectively there appears to be no logical link between the obsession and compulsion. For example, I wont feel right if the jars arent lined up or harm will come to my family if I dont line up all the jars, so the compulsion is therefore lining up the jars.

Intrusive thoughts: the intrusive thought is usually highly distressing and repetitive. Common examples may include thoughts of perpetrating violence towards others, harming others, or questions over ones character or deeds, usually in conflict with the persons true values. An example would be: I think I might hurt my family, which in turn leads to the compulsion of avoiding social gatherings.

Hoarding: the intrusive thought is the overvaluing of objects or possessions, while the compulsion is stashing or hoarding these items and refusing to let them go. For example, this newspaper may come in useful one day, therefore, the compulsion is hoarding newspapers instead of discarding them the next day.

Source: Dr Robert Chandler, clinical psychologist at Lighthouse Arabia

Common OCD symptomsand how they manifest

Checking: the obsession or thoughts focus on some harm coming from things not being as they should, which usually centre around the theme of safety. For example, the obsession is the building will burn down, therefore the compulsion is checking that the oven is switched off.

Contamination: the obsession is focused on the presence of germs, dirt or harmful bacteria and how this will impact the person and/or their loved ones. For example, the obsession is the floor is dirty; me and my family will get sick and die, the compulsion is repetitive cleaning.

Orderliness: the obsession is a fear of sitting with uncomfortable feelings, or to prevent harm coming to oneself or others. Objectively there appears to be no logical link between the obsession and compulsion. For example, I wont feel right if the jars arent lined up or harm will come to my family if I dont line up all the jars, so the compulsion is therefore lining up the jars.

Intrusive thoughts: the intrusive thought is usually highly distressing and repetitive. Common examples may include thoughts of perpetrating violence towards others, harming others, or questions over ones character or deeds, usually in conflict with the persons true values. An example would be: I think I might hurt my family, which in turn leads to the compulsion of avoiding social gatherings.

Hoarding: the intrusive thought is the overvaluing of objects or possessions, while the compulsion is stashing or hoarding these items and refusing to let them go. For example, this newspaper may come in useful one day, therefore, the compulsion is hoarding newspapers instead of discarding them the next day.

Source: Dr Robert Chandler, clinical psychologist at Lighthouse Arabia

Common OCD symptomsand how they manifest

Checking: the obsession or thoughts focus on some harm coming from things not being as they should, which usually centre around the theme of safety. For example, the obsession is the building will burn down, therefore the compulsion is checking that the oven is switched off.

Contamination: the obsession is focused on the presence of germs, dirt or harmful bacteria and how this will impact the person and/or their loved ones. For example, the obsession is the floor is dirty; me and my family will get sick and die, the compulsion is repetitive cleaning.

Orderliness: the obsession is a fear of sitting with uncomfortable feelings, or to prevent harm coming to oneself or others. Objectively there appears to be no logical link between the obsession and compulsion. For example, I wont feel right if the jars arent lined up or harm will come to my family if I dont line up all the jars, so the compulsion is therefore lining up the jars.

Intrusive thoughts: the intrusive thought is usually highly distressing and repetitive. Common examples may include thoughts of perpetrating violence towards others, harming others, or questions over ones character or deeds, usually in conflict with the persons true values. An example would be: I think I might hurt my family, which in turn leads to the compulsion of avoiding social gatherings.

Hoarding: the intrusive thought is the overvaluing of objects or possessions, while the compulsion is stashing or hoarding these items and refusing to let them go. For example, this newspaper may come in useful one day, therefore, the compulsion is hoarding newspapers instead of discarding them the next day.

Source: Dr Robert Chandler, clinical psychologist at Lighthouse Arabia

Common OCD symptomsand how they manifest

Checking: the obsession or thoughts focus on some harm coming from things not being as they should, which usually centre around the theme of safety. For example, the obsession is the building will burn down, therefore the compulsion is checking that the oven is switched off.

Contamination: the obsession is focused on the presence of germs, dirt or harmful bacteria and how this will impact the person and/or their loved ones. For example, the obsession is the floor is dirty; me and my family will get sick and die, the compulsion is repetitive cleaning.

Orderliness: the obsession is a fear of sitting with uncomfortable feelings, or to prevent harm coming to oneself or others. Objectively there appears to be no logical link between the obsession and compulsion. For example, I wont feel right if the jars arent lined up or harm will come to my family if I dont line up all the jars, so the compulsion is therefore lining up the jars.

Intrusive thoughts: the intrusive thought is usually highly distressing and repetitive. Common examples may include thoughts of perpetrating violence towards others, harming others, or questions over ones character or deeds, usually in conflict with the persons true values. An example would be: I think I might hurt my family, which in turn leads to the compulsion of avoiding social gatherings.

Hoarding: the intrusive thought is the overvaluing of objects or possessions, while the compulsion is stashing or hoarding these items and refusing to let them go. For example, this newspaper may come in useful one day, therefore, the compulsion is hoarding newspapers instead of discarding them the next day.

Source: Dr Robert Chandler, clinical psychologist at Lighthouse Arabia

Common OCD symptomsand how they manifest

Checking: the obsession or thoughts focus on some harm coming from things not being as they should, which usually centre around the theme of safety. For example, the obsession is the building will burn down, therefore the compulsion is checking that the oven is switched off.

Contamination: the obsession is focused on the presence of germs, dirt or harmful bacteria and how this will impact the person and/or their loved ones. For example, the obsession is the floor is dirty; me and my family will get sick and die, the compulsion is repetitive cleaning.

Orderliness: the obsession is a fear of sitting with uncomfortable feelings, or to prevent harm coming to oneself or others. Objectively there appears to be no logical link between the obsession and compulsion. For example, I wont feel right if the jars arent lined up or harm will come to my family if I dont line up all the jars, so the compulsion is therefore lining up the jars.

Intrusive thoughts: the intrusive thought is usually highly distressing and repetitive. Common examples may include thoughts of perpetrating violence towards others, harming others, or questions over ones character or deeds, usually in conflict with the persons true values. An example would be: I think I might hurt my family, which in turn leads to the compulsion of avoiding social gatherings.

Hoarding: the intrusive thought is the overvaluing of objects or possessions, while the compulsion is stashing or hoarding these items and refusing to let them go. For example, this newspaper may come in useful one day, therefore, the compulsion is hoarding newspapers instead of discarding them the next day.

Source: Dr Robert Chandler, clinical psychologist at Lighthouse Arabia

Common OCD symptomsand how they manifest

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Is AI racist? Why more diversity is needed in the field of data science - The National

The government will also have to earn back trust. The trust deficit is why so few of my neighbours in Brixton, south London, are vaccinated. A lack of trust also caused an uproar over this springs scheme to pool Englands 55 million patient records into a permanent data lake, and to give companies access. Ministers hoped, perhaps, that COVID gave them a political mandate for a data free-for-all, in which companies could be readily let in to play in NHS records. They were wrong. Well over a million people opted out of that scheme, which again after a Foxglove-led coalition threatened legal action has since been kicked into the long grass.

Make no mistake: Palantir is still looking to bid for health contracts in the UK. Anyone concerned about trust in the NHS should join our demand that the firm be kept well away from health care. But the debates to come will be harder. Our government plainly hopes to hop on a tech moguls rocket towards an ever-closer union between Big Tech and the NHS, ripping up critical data protection and procurement laws to get there. Ministers have said they want to open up Englands health data for tech firms to go prospecting for gold in. Whether their standard for public benefit matches yours well, thats a question of trust.

It doesnt have to be this way. Across the NHS, in universities and hospitals, people are forming alternative ideas to an NHS run by and for big tech companies. Broadly, they involve more transparent, locally controlled, public-spirited uses of health data. This will, of course, take a fight. But the public holds more cards than you might think. Its politically very hard to seize peoples health records without their say-so. And when millions opt out of data-sharing, the dataset changes its less useful, and less commercially valuable. If people dont trust the government with the NHS or their health data, three-word slogans like data saves lives wont fill that gap. The millions who have opted out will never opt back in.

A better way is in sight if we demand it. Dont sleep on it you know Palantir wont.

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The UK government has ended Palantir's NHS data deal. But the fight isn't over - Open Democracy