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Projects keep failing, so what’s the
problem?
Projects
are about delivering an outcome that fixes a business need. Others suggest
projects are to take advantage of an opportunity. Those opportunities usually
are to fix a perceived problem. Those perceptions to fix that future problems still
needs a project to implement them and solve it. Those projects still need to be
justified.
Projects
fail for many reasons, building on shaky foundations will usually end in failure.
That foundation is a well-defined and explained problem. Problems need to be
clearly identified, stating how the proposed solution will fix it, and showing
a value proposition to the organisation, customer or both. A quantifiable and
demonstratable benefit, as most businesses are there to make money or provide
better services, why would you be doing it?
Projects
need to argue the logic for investing both time and money, more importantly
what the pay back will be. Business is about making money, providing a service,
or both. Funds are usually limited, rarely having a lack of opportunity to be
spent on the many challenges facing organisations.
What is the Business Case for this
project?
At
first, an organisation wants to understand the why of a project. Closely after
that is the how will it be achieved, and how much. But there needs to be a
compelling reason for carrying out the project. Often projects put the cart
before the horse. In other words, they have a new or updated product that will
future proof their organisation, addressing many of the perceived issues that
could be addressed by the many of the new features on offer all presenting
sound arguments. In a world of unlimited resources and funds that would not be
a problem, but that is not the case. Money and resources are a factor of every
business and they are not always limitless.
Projects
that fail are usually proposed with all the good intents, the arguments of the
new features all sound good. The biggest issue is that no one hears the same
benefits. This results in different stakeholders with different expectations.
As the projects progress it becomes a feature fest. More is better, right? But
time elapses, costs increase, expectations having been ill defined results in
no one being happy. Time and money start to run out, results are not achieved
and the project grinds to a halt. Does this sound familiar?
Questions are raised
Why
are these projects failing? What went wrong? We had all the governance in place
and it seemed to be working fine, then it all went south. I just don’t understand
what happened?
The
problem is there was no real problem being addressed, or that problem was
perceived and not correctly identified. Problems form the foundations of a business
case, needing to be clearly identified, quantified and expressed in a manner
that all parties can agree. Business cases need to clearly explain the intended
problem to be addressed. Identifying problems and explaining the consequences if
they were not addressed. Then describing the benefits that would result in
fixing them, more importantly how that would be proved.
When
defining the problems and proposed benefit, there needs to be an understanding of
the following:
·
Why invest? – Describe how this investment will benefit the
organisation
·
The Rational – What is the logic for this investment, how
will it be tested and proved that it has delivered the expected results
·
Who feels that this is a problem – gather views from all
appropriate stakeholders within the organisation through discussion with the
subject matter experts.
Defining the problem
Often
what is perceived as a problem is usually not the problem. To find the real
problem you will need to carry out “root cause analysis”. A good example of
this is a technique commonly referred to as the “5 Whys”. This is an iterative
interrogative technique used to explore the cause and affect relationships
underlying a problem.
What sort of questions should you be
asking?
A
famous quote of Einstein was:
“If
I had an hour to solve a problem and my life depended on the solution, I would
spend the first 55 minutes determining the proper question to ask…
for once I know the proper question, I could solve the problem in less than
five minutes.”
What
stakeholder say is a problem does not necessarily reflect the root cause that
created the problem. Every stakeholder potentially will have different issues
which they consider a problem. Your job is to dig, finding the root cause. You
need to identify both the cause and the consequence to any issue raised as
potential problems. A simple test is called the “so what?”, Similar to the “5
Whys”, which will be covered a little later.
Is
there any evidence that confirms the cause and effect of the identified
problem? What is the priority? Does it need to be addressed now or could it
wait? Is the issue specific to what you are looking at, or should that
perspective be broader?
Example of 5 Whys
“..
the finance director could not understand why his maintenance costs were
increasing on the factory floor. He had sent a directive to the department to
cut costs. He decided to venture down to the factory floor to speak with the
manager and better understand these increases. (His perceived problem)
..
as the finance director was walking through the factory he noticed a pool of
water on the floor. He called a maintenance staff member to inquire about the
water.
·
Why is there a pool of water here on the floor? The staff
member pointed out that one of the pipes above was faulty and leaking. (Maintenance
perceived problem) The director then asked for the manager,
·
Why was that pipe leaking? The manager pointed out the
replacement washer had not sealed properly. Again, the director then asked,
·
Why did the washer not seal properly? The manager suggested the
washer had possibly failed. The director then asked,
·
Why did it fail. The manager then suggested the washers were
cheap and that they had a tendency not to last too long. Again, the director
asked,
·
Why were we using cheap washers? I was following the budget directive
to cut my maintenance costs. We then sourced alternatives as our previous
washers were too expensive.
The
director had found the root cause. In this case there were several perceived
problems. The director had a problem with his costs of maintenance, the staff
member had a faulty pipe and the manager had issues with cheap washers. At
first replacing the pipe potentially could have fixed the problem. But as it
was not the root cause it would have resulted in an expensive fix and the pipe
potentially would have leaked again because of the washer. The root cause for
the pipe was the use of a cheaper alternative. It also highlighted the cost
increase to maintenance had indirectly been because of a cost cutting
directive.”
To
define a problem, you will need to consider the downstream effects of what you
and other stakeholders consider to be the problem and what it means to your
organisation. There are two parts to a problem what has caused it and what are
its consequence? Understanding these causes will help you chose how you
respond. The consequences of a problem will help in identifying relevant
benefits. Showing that investment can work to the objectives in this case,
those objectives will later provide an opportunity to identify alternatives.
Problems
that are not well-defined make it harder for decision makers, reducing the
chance of success. This can result in projects that results in less than fit
for purpose results. Either too little in the way of funds and resources, or
too many working on low-priorities. The worst case is lack of resources to
solve a major challenge.
Success
is through clearly understanding the problem and benefits from the beginning. This
will enable everyone to be on the same page, agreeing to the same expectations
and results. Aligning results to the organisations priorities and effectively
addressing the right problem. The idea of a well understood problem is that it
will potentially highlight an opportunity for better results.
Explaining the problem
This
is the elevator pitch, if you were trapped in the elevator with the key
stakeholder who was the approver of the funds needed. How do you relate the
issue in 90 seconds? That pitch needs to clearly identify the issue, providing
the evidence that supports your statement and the solution with definable
measure of success.
Problems
that are ill defined can result in benefits that do not align and undermine your
entire argument for the case. Businesses want to understand how much of a
problem it is? The goal being a call to action. It should have both cause and
consequence, answering both the ‘Why?’ And…’ Questions logically linked. A
great starting point is identifying the consequences of doing nothing?
Your
pitch will never be perfect, potentially changing as more information is
gathered. It will be tested against evidence and morph from its original state,
be prepared for change. The challenge is to go into this exercise without any preconceived
solutions. As further evidence is presented it will develop your understanding
and result in a better result, and a stronger foundation to build your case.
Mistakes in identifying problems
Many
people go into identifying problems sure of the solution, especially when it
comes to technology. In the technology space providers and IT specialist believe
their solutions will provide the answers to any problem. Its just a matter of
shoe-horning those problems into that solution.
Avoid
simply identifying the problem as a system failure, this has a tendency to drive
the results which usually does not align to the facts and the issue. Again, go
back to “so what?”. What is the evidence that will give you a confidence that a
problem exists? You must present that evidence to explain your rational.
Always
note where you found the evidence as you develop your pitch, it’s always harder
if you try to retrofit a problem with evidence. One of the best tests I would
use is called the “Mum Test”, find someone who is not related to the case to
read the pitch and benefits, ask them, “Does this make sense?”. For me, when I
was an interface designer I would as my mother if she could carry out a specific
task using that interface. With no instructions, I would see what she would do
to achieve the results. The idea is to remove the element of assumption, as we
don’t always know who the audience will be, we need to make sure it is clear
without having to be there to explain.
Benefits, what are they?
When
you understand the problem and its consequence most people will understand the
benefit of doing something about it. A benefit gives a measurable improvement,
showing the value gained. The consequence of a problem helps to identifying the
relevant benefit that lead to your objective.
They
should clearly align to the problem that links to the results your organisation
is looking to achieve. Explain the impact which credits to the solutions.
Justify the cost of both money and effort which are supported by demonstratable
returns.
Measuring those returns
The
best way to show a return is by having a measure based on current and future states.
Everyone will have a different measure of value, so there needs to be some more
good questions.
This
is the old “WIIFM”, (What’s in it for me). How are you going to show the value
you are declaring?
·
What will be the return to the organisation or its customers?
·
How will you measure and prove that benefit?
·
How will you show the connection of the benefit to the results?
These
are just a few points to consider when defining and showing benefits in a
project. These measures are to be defined with your stakeholders as they are
the people who will confirm the returns on investment (ROI). They need to be
identifiable, measurable and proven.
Prioritisation
As
you define your problems and benefits there is a need to priorities each of
them. It’s not an exercise in the level of investment is directed to fix the
problem but more enabling better decisions between available alternatives,
making sure you get the best bang for your buck. This will enable focus and to
direct both funds and effort in future, more importantly you can control scope.
These
priorities will enable better and more directed decisions when you may not get
all the funds you expect. A small problem which has Signiant results for an
organisation or its customers, compared to a large problem which has limited
impact will give a signal of investment. But it raises the question to the
larger problem, has it been clearly identified? And are the consequences fully understood.
All
of these are good questions and will need further examination. This is not an
exact science, but it is a major step in the right direction.
What tool can help with this process?
A
technique used to ensure robust discussion and thinking is carried out up-front
in a project is Investment Logic Mapping (ILM). It is a great tool to use
before a solution is identified and before any investment decision is made.
ILM
provides a way of identifying problems that need to be addressed. This will
identifying benefits hoped to be gained, more importantly how the project will confirm
the rational, showing the realisation of those benefits. The ILM tool is used
for complex investments but is recommended for any project and will enable the
ability to communicate that information on a single page.
Should you use ILM?
Many
organisations trigger this process based on the investment. It is something
that is not compulsory but is recommended especially for complex, high-risk or multiparty
proposals.
In
practice it should form the start of all projects, as the output forms the
foundation of your entire business case. The degree and level that you engage
is determined on the size, complexity and value of the project, but the format
and principles will always be useful to defining your problems and how the
benefits will address them.
As
the project progresses it will increase the project focus and clarity, helping
in defining an agreed scope and result, which will save debate and discussion
later in the project. It will become a powerful tool that will provide you
leverage in justifying your expenses of both funds and effort.
How does it work?
Using
a facilitator, key stakeholders in a couple of workshops will discover:
·
Your problems and consequences, then
·
The outcomes and benefits.
·
These workshops will build an alignment on the purpose of the
investment, it may not necessarily lead to an agreement, but it will be a
start.
What can you expect from an ILM
workshop?
You
should expect to have a single page flowchart that will be written in plain
English. It will define your problems to be addressed, potential benefits of
your investment, and how you will confirm those benefits. It will become the
underpinning logic around your project investment.
ILM
workshops will be a series of time-limited engagements up to two hours each. It
will bring together the accountable stakeholders for the benefits realisation.
It should be low-cost and low-effort that will produce new information. It will
bring together all available information to enable a better understanding,
leading to better results.
Problem
owners need to prepare by checking their evidence, identifying the right
stakeholders for the workshops and offering their opinion and expertise. The
right stakeholders are those who have identified and understood the business
problems, provide the evidence the problem is real, and is responsible for
delivering the benefits. Other stakeholders are those people responsible for
giving advise around the investment to the project. This will increase the
value of the workshops, avoiding the risk of having to start again. Most would
have already been engaged from the start.
The
problem owner needs to drive the effort, talking with the right stakeholders
and their willingness to contribute will lead the to the right pitch when
presenting your case.


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The Smart Nation and Digital Government Office (SNDGO) and a major cloud computing company have announced the launch of the Artificial Intelligence Government Cloud Cluster (AGCC), a comprehensive platform designed to accelerate AI adoption in Singapore’s public sector, advance local applied AI research efforts and support the growth of the local AI startup ecosystem.
The AGCC has been implemented by SNDGO and the cloud tech company for usage by Singapore’s government agencies and the research, innovation, and enterprise (RIE) ecosystem. The AGCC is hosted in Singapore in a specialised cloud computing environment.
Agencies can use the AGCC to build and deploy scalable and impactful AI applications rapidly, safely, ethically, and cost-effectively by leveraging an AI technology stack and a vast partner ecosystem of software-as-a-service firms, consultancies, and AI startups. AI technology stack capabilities include:
First, an AI-optimised infrastructure. High-performance A2 supercomputers powered by NVIDIA’s A100 GPUs and hosted in an open, scalable, secure, and energy-efficient infrastructure. This enables cloud developers to train computationally complex AI models at fast speeds while minimising costs and environmental impact.
Customisable first-party, third-party, and open-source AI models follow. A central repository enabling AI practitioners to access pre-trained generative AI models, with built-in features to assist users in customising these models for specific requirements.
The repository contains a wide range of first-party, third-party, and open-source models designed for certain needs. These include models for summarising and translating text in different languages, sustaining an ongoing discussion, converting audio to text, producing, and modifying software code, and generating and repairing written descriptions.
International AI businesses interested in making their foundation models available to Singapore government departments can collaborate with the Cloud computing company to store these models in the repository.
Another category is no-code AI development tools. A Generative AI App Builder enabling developers (especially those with limited technical expertise) to swiftly construct and seamlessly embed chatbots and enterprise search experiences driven by Cloud’s generative AI models.
Finally, there are explainable AI and data governance toolkits. A set of built-in technologies that can assist government agencies in using AI in a secure and responsible manner. This includes features for access control and content moderation, as well as novel mechanisms for incorporating human feedback to improve model performance and the ability to audit the sources of AI model outputs to detect and resolve potential bias and ensure that model behaviour is compliant with regulations.
The Government Technology Agency (GovTech) is Singapore’s first public-sector organisation to use the AGCC. Its Open Government Products (OGP) team has integrated with Vertex AI and is investigating the use of its models in Pair, which are large language model-powered assistants that civil servants can use to help them boost productivity while maintaining the confidentiality of government information.
To help government agencies deploy AI applications as effectively and responsibly as possible, the Cloud tech company will collaborate with GovTech to design and run whole-of-government Digital Academy programmes that will assist agencies in developing in-house data science and AI expertise, developing AI innovation strategies, and implementing data governance best practices.
The programmes will be delivered in a variety of specialised formats to 150,000 public servants from 16 ministries and over 50 statutory boards.
Government agencies in Singapore will be able to use the AGCC and other authorised services through the Government on Commercial Cloud (GCC) 2.0 platform beginning in June 2023. The GCC platform, developed by GovTech, offers agencies a standardised and regulated means to implement commercial cloud solutions.
GCC 2.0, the platform’s second generation, is integrated with cloud-native capabilities and cloud security practices, enabling agencies to access into a larger ecosystem of services and people to accelerate the development of new digital applications.
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State-run Osmania University in Hyderabad has embraced artificial intelligence and machine learning (AI/ML) to revolutionise its attendance marking process. Now, the tedious waiting to sign attendance registers or use biometric systems is no longer required. Employees simply need to enter the building, and their attendance will be automatically recorded. This is made possible through CCTV cameras equipped with AI and ML technologies, which accurately mark employees’ attendance, log-in and log-out times, as well as their entry and exit from the building.
The functionality of these cameras relies on an integrated facial recognition system. Leveraging cognitive AI capabilities, they identify facial biometrics and synchronise them with the current database to accurately document employee attendance. This innovative solution has been implemented as a pilot project in the main administrative building of the university for its employees. In the future, the university plans to expand the deployment of these cameras to other campus facilities, including offices, classrooms, and hostels.
According to an official from the university, under the manual system, employees often mark their attendance in the morning, leave the office, and return later to record their log-out time. However, with the implementation of CCTV camera-based attendance, this process will undergo a significant change. The cameras continuously capture movement and simultaneously store employees’ log details, eliminating the need for manual recording. To achieve this, AI and ML technologies have been integrated into two cameras.
Additionally, the official noted that the CCTV cameras also capture the log-in information of visitors entering the administrative building. This means that the log details of all visitors are systematically recorded in the database, providing a comprehensive attendance tracking system.
The introduction of CCTV-based attendance eliminates the need for manual attendance registers or time-consuming biometric systems, streamlining the entire process. Students simply need to enter the designated areas covered by CCTV cameras, and their attendance is promptly recorded. This not only saves time but also reduces the chances of errors or misinterpretations.
Moreover, these AI-powered cameras not only capture the presence of students but also provide additional functionalities. Some universities have integrated facial recognition systems to ensure authentication and prevent proxy attendance. The cameras analyse facial biometrics, matching them against the existing database to ensure accurate identification. It can also enable the university to track attendance patterns, identify areas that require improvement, and take proactive measures to enhance student engagement and performance.
Most educational institutions across the country are embracing the advancements brought by AI. Numerous schools and colleges have incorporated AI-based learning techniques to simplify the process of education and effectively teach intricate subjects to students. Additionally, AI’s adaptable learning methods assist teachers in providing personalised attention to each student.
Last year, the Indian Institute of Technology Madras (IIT-Madras) and the Tamil Nadu State Department of School Education announced they would collaborate to improve and update the digital learning platform for school students to an assessment-focused Learning Management System. It was deployed in high-tech labs in 6,000 government schools, as OpenGov Asia reported. It aimed to improve the quality of learning for around nine million students.
Education in Tamil Nadu’s schools was previously supplemented through a digital learning platform called the Education Management Information System. Researchers from the Indian Institute of Technology Madras used their AI and data science expertise to come up with ways to improve the way assessments are conducted and develop a framework to disseminate educational material.
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The University of Sydney recently entered into a memorandum of understanding (MoU) with the Australian subsidiary of a pharmaceutical company based in South Korea. The partnership aims to leverage the power of artificial intelligence in identifying potential compounds for accelerated development into treatments for cancers and rare diseases.
Under the MoU, the University’s Drug Discovery Initiative will gain access to the pharmaceutical company’s advanced AI drug development platform, known as Chemiverse. This collaboration will enable the University to harness the capabilities of AI in identifying promising compounds for drug development. Additionally, the company will benefit from collaborating with the University’s esteemed team of researchers and using their cutting-edge drug discovery infrastructure.
The Director of the Drug Discovery Initiative expressed enthusiasm about the collaboration with the company. He highlighted the complexity involved in developing drugs for treating diseases and emphasised the significance of working with Pharos and their advanced artificial intelligence platform, Chemiverse.
The use of Chemiverse in this partnership is expected to greatly enhance the University’s capacity to develop innovative treatments for unmet medical needs. Moreover, the synergies between the platform and the Drug Discovery Initiative will foster innovation and facilitate the establishment of new drug discovery pipelines.
The Drug Discovery Initiative, situated within the School of Chemistry, serves as an interdisciplinary academic network that aims to expedite the early-stage development of drugs by leveraging top-tier individuals, technologies, and tools.
The Pro-Vice-Chancellor (Research Enterprise) emphasised the University’s dedication to translating fundamental research into practical solutions. The partnership with the company is viewed as an opportunity to capitalise on the expertise housed within the Drug Discovery Initiative. Together, they strive to advance the development of potentially life-saving targets for cancer and rare diseases.
The co-CEO of the company’s Australia branch expressed excitement about collaborating with the University and the Drug Discovery Initiative. He said the use of state-of-the-art infrastructure to accelerate drug discovery efforts.
The firm’s Chemiverse platform is a versatile tool that can be employed across the entire spectrum of new drug development, encompassing target discovery to lead compound generation. This advanced platform incorporates a vast amount of big data, approximately 230 million data points, and uses advanced algorithms to facilitate the drug development process.
The company is actively engaged in ongoing research and development as well as commercialisation efforts using the Chemiverse platform. They are currently working on approximately 10 pipeline projects, which include the development of a treatment called “PHI-101” for acute myeloid leukaemia. Notably, PHI-101 is currently undergoing phase 1b clinical trials.
On the other hand, the Drug Discovery Initiative plays a prominent role in the development of new compounds and the identification of collaborative pipelines. They are highly active in their pursuit of advancing drug discovery and forging partnerships in this field.
In March, the NSW Government provided funding for the establishment of the NSW Organoid Innovation Centre. This state-of-the-art facility is a collaborative initiative involving multiple institutions. It focuses on using cutting-edge stem-cell techniques to expedite the process of drug discovery and design.
The pharmaceutical company, earlier this year, became a part of the Sydney Knowledge Hub, which serves as a startup incubator and coworking space located at the University of Sydney. This strategic collaboration aims to foster partnerships and facilitate seamless collaboration between industry and the research community in Sydney.
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With each passing day, technology continues to evolve and flourish in our society. Its rapid advancement encompasses and profoundly impacts numerous aspects and industries, compelling professionals to adopt artificial intelligence-driven (AI-driven) solutions to augment their productivity and efficiency. This technology has inevitably penetrated the realm of education, enabling teachers to utilise generative AI in assessing pupils’ work.
In light of its subjectivity and limited capabilities in assessing complex academic content, the Ministry of Education in New Zealand recommends that teachers carefully consider using generative AI technology to mark pupils’ work.
The ministry strives to uphold fair and accurate evaluation methods that align with educational standards and objectives, “Without understanding the basis for seeing inside the algorithm, this can be leading to discrimination and unfairness.”
Furthermore, there are instances where technology can be fallible due to the absence of human intervention (human touch). Generative AI systems trained solely on internet data may need more exposure to the specific work produced by children and young individuals, resulting in a limited understanding of what is suitable and expected from them, leading to a limited understanding of what is deemed appropriate and expected from this demographic.
Victoria University Senior Lecturer in software engineering, Simon McCallum, said that he agreed teachers should be wary of using generative AI for marking pupils’ work. However, McCallum believed generative AI tools would eventually be very good for grading students’ work.
Utilising generative AI, in the educational setting is recommended by employing it purposefully and judiciously. Ultimately, teachers can leverage its potential to teach students critical literacy skills, specifically empowering learners to question the accuracy of the information they encounter and to identify bias. With generative AI as a valuable resource, the educational experience becomes a dynamic and engaging journey of exploration and critical thinking.
Technological evolution can bring various advantages and disadvantages. However, humans cannot entirely rely on it, especially considering the technology has yet to reach its potential fully. Vaughan Couillault, president of the Secondary Principals Association, says, “There are many advantages to having machines automate certain tasks, but the quality is not yet at the desired level.”
As advancements in generative AI persist, its reach extends beyond its initial domains, finding applications in diverse fields and sectors. Several countries are now witnessing firsthand the transformative impact of generative AI on traditional business, even government policy models.
In New Zealand alone, there is a strong emphasis on promoting technology integration in education, with initiatives designed to support teachers adapting to technological advancements. One such initiative is the tech programme for teachers, which aims to equip educators with the necessary knowledge and skills to incorporate technology into their teaching practices effectively.
These initiatives aim to empower teachers to impart their newly acquired knowledge to students, especially those who are digitally inclined. By doing so, these programs foster a culture of technological fluency and inspire the next generation to embrace the digital world. One of the initiatives is the tech program for teachers, which aims to provide educators with the necessary knowledge and skills to integrate technology into their teaching practices effectively.
The curriculum emphasises the significance of fostering critical literacy, including digital literacy. Teachers can leverage generative AI by creating texts and incorporating them into lessons to develop students’ critical literacy abilities. Additionally, teachers can utilise a series of texts to enhance students’ understanding of the effective use of prompts.
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Given the recent rapid development of artificial intelligence (AI), the Ministry of Digital Affairs (moda) announced that it has become an official partner of an international non-governmental organisation to ensure the alignment of AI applications with the interests of the public and to develop the necessary application services for society.
As a member of the “Alignment Assemblies” project, moda’s global and public objectives are to assist Taiwan in building public consensus regarding the needs and risks of AI and to collectively address the “Alignment Problem” of AI.
Beginning in July of this year, the moda intends to influence the direction of AI development through Ideathons, employing a citizen participation and deliberation model and using Taiwan as a test bed.
The moda emphasised that the international non-governmental organisation supports the technology that incorporates social development, industrial advancement, and public confidence. It believes that the development of AI should prioritise ethics and the public interest.
During the Democratic Summit in March of this year, the moda, led by Minister Audrey Tang, launched this initiative to create a global consensus among people and ensure the alignment of AI with human values. By participating in this initiative, the moda hopes to promote digital democracy and global partnerships while fostering a diverse and inclusive digital culture for the development of AI in Taiwan.
The moda announced that it will initiate the “Democratising AI Futures” dialogue through Ideathons and invite public participation in the third quarter of this year as well as organise seminars to discuss how to respond to the development of generative AI.
Minister Audrey explained that AI has brought about profound social changes and that issues such as algorithms, intellectual property, technological ethics, public services, and social impact have garnered significant attention, posing new challenges to democratic governance.
In response to the social concerns raised by the trend of generative AI, moda is actively drafting the “Basic Law on Artificial Intelligence.” The moda also expects that policymakers and technology developers will have access to vital information to ensure that the development of AI serves the public interest.
In the fast-changing technological world, fostering consensus on the requirements and hazards associated with AI is essential. As AI continues to evolve and permeate numerous elements, it is critical to ensure that its development and deployment are in line with the interests and values of society.
Building consensus allows for the identification and prioritisation of ethical considerations in AI development. It enables stakeholders to address possible issues such as bias, privacy concerns, and job displacement cooperatively, as well as cooperate towards developing AI systems that adhere to ethical norms and protect human rights.
Also, achieving consensus on AI enables policymakers to make educated decisions when developing legislation and policies. Policymakers may establish comprehensive frameworks that balance innovation, social demands, and possible risks connected with AI technology by considering the different perspectives and concerns of the public.
Building consensus aids in the establishment of public trust and acceptance of AI systems. When the public participates in AI debates and decision-making processes, people feel more empowered and are more inclined to trust and adopt AI applications that are consistent with their values and meet their requirements.
Consensus-building aids in resolving biases and guaranteeing fairness in AI algorithms and systems. Potential biases can be recognised and minimised by integrating a diverse variety of stakeholders, including marginalised populations and underrepresented groups, resulting in more equal opportunities in AI systems.
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According to a recent guideline, China intends to reform science education in primary and secondary institutions over the next three to five years to vastly improve students’ science literacy.
New technologies such as artificial intelligence (AI) and virtual reality (VR) will be utilised to teach students how to conduct experiments and to close the educational resource gap in underdeveloped schools and regions, according to a guideline issued by the Ministry of Education and seventeen other departments.
Local authorities must ensure that science courses in elementary and secondary schools adhere to their curricula, and instructors of other subjects should not interfere with the time used to teach these courses.
Each school must also have a vice-principal responsible for science education and a science student counsellor. The guideline encourages schools to invite experts and scientists to deliver lectures on campus and to organise student trips to scientific sites.
In addition, science education should become a central subject in after-school services provided by schools, and activities like science lectures and experiments should be conducted to pique students’ interest.
Universities, research institutes, science centres, and museums are expected to improve their service to elementary and middle school students. The guideline also encourages businesses, particularly those focusing on cutting-edge technology, to offer equipment, software, and personnel to schools in underdeveloped regions and to invite elementary and middle school students to visit and observe how scientific knowledge is applied in the real world.
The report of the 20th National Congress emphasised the need to further integrate the development of education, science, and talent, and this document represents another step in China’s efforts to establish an education- and science-driven nation.
In the meantime, engineering and science majors have acquired popularity among Chinese university students, with AI topping the list of the most popular majors for incoming freshmen.
Wei Yungang, director of the experimental education centre at Beijing Normal University’s School of Artificial Intelligence, stated that while larger cities provide superior science education, smaller cities and rural schools lack the necessary resources. He stated that it was notable that the guideline emphasised closing the education divide using AI.
With the advent of AI, schools must assist students in adopting the technology so they can develop the ability to solve problems using AI, he said, adding that AI proficiency will be crucial for future global competition.
Wei stated that less-developed regions can gain access to high-quality educational materials using AI and digital education. While more work is required to integrate AI into classroom learning and instruction, it will, ultimately, result in significant changes to the way students are educated.
“The vast amount of data and information gathered daily in classrooms can be analysed by AI so that we can gain a better understanding of how students learn and how teachers and education authorities can apply this knowledge in policymaking,” he said.
Additionally, the Chinese mainland and Hong Kong strengthen educational cooperation by facilitating exchanges of teachers and students for mutual learning. The largest delegation since travel resumed is a group of 200 Hong Kong principals and teachers on an exchange tour to Beijing.
The visit seeks to provide insights into the mainland’s socioeconomic progress and basic education achievements. Officials emphasised the need of developing talent and lead pupils towards a better understanding of the country.
The trip aims to enhance experience sharing, leverage teacher training platforms, and strengthen basic education cooperation between Hong Kong and Beijing. The effort is in line with national education reform and promotes student national identification.
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In our interconnected and globally integrated world, the emergence of various pathogens is just a plane or ship journey away, and their impact can be substantial on both local and global economies. In light of this issue, Professor of Data Science, Alex Gavryushkin, is co-leading the new research exploring the algorithm to respond to a biosecurity outbreak swiftly and effectively.
Associate Professor Gavryushkin said that agriculture is vital for Aotearoa New Zealand’s economy and is the basis of global exports. The agricultural sector produces 40% of New Zealand’s exports. Agriculture is the backbone of rural economies, providing employment opportunities for farmers and farmworkers and supporting industries such as agribusinesses, equipment manufacturers, and food processing companies.
However, agriculture is not only limited as a significant source of employment in several rural areas. Its performance also influences the success of urban areas and many secondary industries which depend on it, increasing economic well-being and sustainability, influencing their growth, infrastructure development, and quality of life.
As we can see, agriculture brings a significant impact on New Zealand itself. People must take it seriously to prevent highly contagious viral infections such as foot and mouth disease (FMD). If agriculture were to be affected by such an outbreak, it could potentially throw the national economy into a recession, causing losses upward of NZ$16 billion.
Professor Gavryushkin strongly emphasises the significance of making highly accurate predictions regarding the potential spread of an outbreak. This accuracy is crucial in facilitating policymakers to foster their decision-making processes and implement effective measures to mitigate and control the outbreak’s impact.
By utilising advanced algorithms capable of dynamically updating results in real time, the research seeks to provide policymakers with up-to-date and reliable information about the evolving nature of the outbreak.
It includes predicting the areas at the highest risk of transmission and identifying potential hotspots, enabling policymakers to allocate resources strategically and implement targeted interventions to limit the spread of the disease
This research has the potential to significantly enhance the ability to respond swiftly and effectively to outbreaks, thereby safeguarding communities and facilitating a more efficient and proactive approach to public health management.
Afterwards, he is embarking on collaborating with the University of Auckland’s Dr Remco Bouckaert and partners from Massey University and the Ministry for Primary Industries (MPI) for doing research in terms of developing a new type of algorithm to improve outbreak response by providing more precision and accurate results.
The objective of the research is to create algorithms that can dynamically update results in real-time, eliminating the need to restart computations from the beginning when large volumes of new data are received. Instead, the algorithms will revise previous calculations and adjust predictions as necessary.
The current algorithm system presents policymakers with only one scenario, based on it being statistically the most probable. Its transmission tracing lacks the ability to effectively handle the continuous influx of new data, which is common during an ongoing outbreak when the transmission tree rapidly expands in size.
According to Associate Professor Gavryushkin, establishing a solid infrastructure for biosecurity algorithms will greatly enhance their ability to proactively address potential issues in the future. By conducting intricate and time-intensive pre-computations well in advance, including prior to outbreaks and concurrently with them, they can significantly mitigate challenges that may arise down the line. This proactive approach ensures that comprehensive preparations are in place, enabling a more efficient and effective response to biosecurity threats.