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ICCIES 2025 – Nơi hội tụ của những chuyên gia hàng đầu

Hội thảo đã thu hút sự tham gia của đông đảo các nhà nghiên cứu, học giả và chuyên gia từ khắp nơi trên thế giới, tạo nên một không gian trao đổi, học hỏi và kết nối vô cùng sôi nổi.

Con số ấn tượng:

• Hơn 200 bài báo khoa học từ các tác giả trên toàn thế giới
• 36 quốc gia tham gia, bao gồm Ba Lan, Đức, Ý, Hoa Kỳ, Nhật Bản, Hàn Quốc, Ấn Độ, Nigeria…
• 115 bài báo chất lượng cao được chấp nhận sau quá trình phản biện nghiêm ngặt
• 4 tập kỷ yếu được xuất bản bởi Nhà xuất bản Springer trong chuỗi CCIS, chỉ mục trên Scopus

GS-TS Costin Badica, Giám đốc Trường đào tạo tiến sĩ “Constantin Belea” thuộc ĐH Craiova (Romania), trình bày tham luận tại hội thảo

Những diễn giả nổi bật và bài thuyết trình đặc sắc

Đặc biệt, hội thảo còn có sự góp mặt của các diễn giả hàng đầu với những bài phát biểu chính đầy cảm hứng. Chúng tôi vô cùng ấn tượng với các chủ đề như:

Những keynote speech được chú ý:

• “Học máy có thể giải thích và bảo vệ quyền riêng tư để hỗ trợ ra quyết định dựa trên dữ liệu” – Một chủ đề cực kỳ thời sự trong bối cảnh bảo mật dữ liệu ngày càng được quan tâm
• “Trí tuệ tập thể cho chất lượng không khí đô thị” – Thể hiện ứng dụng AI trong giải quyết các vấn đề môi trường và phát triển bền vững

Những bài báo cáo này không chỉ mở ra các hướng nghiên cứu mới mà còn cho thấy tiềm năng ứng dụng to lớn của trí tuệ tính toán trong việc giải quyết các vấn đề thực tiễn của xã hội.

Chương trình học thuật đa dạng và chất lượng

Xuyên suốt ba ngày diễn ra sự kiện, hàng loạt các chủ đề nóng hổi và mang tính tiên phong trong lĩnh vực Trí tuệ tính toán đã được trình bày và thảo luận sâu rộng. Các phiên báo cáo đa dạng, bao trùm các lĩnh vực như:

Các lĩnh vực nghiên cứu chính:

• Học máy (Machine Learning) – Các thuật toán AI tiên tiến và ứng dụng thực tế
• Thị giác máy tính (Computer Vision) – Xử lý ảnh và nhận dạng đối tượng thông minh
• Hệ thống thông minh (Intelligent Systems) – Tự động hóa và ra quyết định thông minh
• Xử lý ngôn ngữ tự nhiên (Natural Language Processing) – Giao tiếp người-máy
• Internet vạn vật (IoT) và Mạng không dây – Kết nối thông minh trong thời đại số

Toàn cảnh phiên khai mạc Hội thảo ICCIES 2025

Opus Solution – Cam kết cho sự phát triển của công nghệ và nhân tài Việt

Với sứ mệnh tiên phong ứng dụng công nghệ và đóng góp vào sự lớn mạnh của nguồn nhân lực chất lượng cao, việc đồng hành cùng ICCIES 2025 là một minh chứng rõ nét cho cam kết của Opus Solution. Chúng tôi tin rằng, việc tạo ra những sân chơi học thuật chuyên nghiệp như ICCIES là con đường ngắn nhất để thúc đẩy đổi mới sáng tạo, cập nhật các xu hướng công nghệ toàn cầu và nâng cao năng lực cho cộng đồng công nghệ Việt Nam.

Lời kết

Hội thảo Quốc tế ICCIES 2025 đã khép lại nhưng dư âm và những giá trị mà nó mang lại chắc chắn sẽ còn lan tỏa. Opus Solution xin gửi lời chúc mừng chân thành đến Ban tổ chức – Trường Đại học Tôn Đức Thắng và các đối tác quốc tế – đã tạo nên một sự kiện thành công và đầy ý nghĩa.

Chúng tôi mong muốn sẽ tiếp tục được đồng hành cùng những sự kiện khoa học, công nghệ đỉnh cao trong tương lai, chung tay xây dựng một cộng đồng công nghệ Việt Nam ngày càng vững mạnh và vươn tầm thế giới mang đến các giải pháp chuyển đổi số toàn diện, giúp doanh nghiệp nâng cao năng lực cạnh tranh trong kỷ nguyên số:

• Giải pháp SharePoint: Là một SharePoint Company Vietnam hàng đầu, chúng tôi chuyên xây dựng hệ thống SharePoint Intranet và SharePoint Portal mạnh mẽ, cung cấp một SharePoint Solution tối ưu cho mọi doanh nghiệp.
• Văn phòng điện tử eOffice: Tự động hóa quy trình với Tasken eOffice. Trải nghiệm eOffice demo để khám phá giải pháp eOffice Solution hàng đầu Việt Nam.
• Nền tảng Microsoft: Tối ưu hiệu suất với Microsoft365 Vietnam và khai phá sức mạnh đám mây với các Azure solutions được tư vấn bởi đội ngũ Azure consultant in Vietnam chuyên nghiệp.

• Phân tích dữ liệu & Tư vấn: Đưa ra quyết định chiến lược với dịch vụ Vietnam Power BI Consultant service và nhận tư vấn chuyên sâu từ đội ngũ Vietnam business consultant giàu kinh nghiệm của chúng tôi.

The post Recap ICCIES 2025: Diễn đàn khoa học đẳng cấp thế giới tại Việt Nam appeared first on Opus Solution.

Trong bối cảnh chuyển đổi số đang diễn ra mạnh mẽ toàn cầu, việc xây dựng một văn phòng “không giấy”, tự động hóa quy trình và tối ưu hóa nguồn lực đã trở thành yếu tố sống còn của doanh nghiệp. Và trong hành trình ấy, Tasken eOffice – sản phẩm công nghệ đột phá từ Opus Solution – đã viết nên câu chuyện thành công đáng tự hào khi được vinh danh hạng 5 sao tại Giải thưởng Sao Khuê 2025.

Phóng sự hậu kỳ vừa được phát sóng không chỉ ghi lại khoảnh khắc vinh quang mà còn tái hiện trọn vẹn hành trình nỗ lực không ngừng, sự sáng tạo và tinh thần tiên phong của đội ngũ phát triển. Đây là minh chứng rõ nét cho câu chuyện “Made in Vietnam” – khi đam mê và tài năng Việt Nam có thể tạo nên những giải pháp công nghệ vượt tầm quốc tế.

Chi tiết phóng sự: https://youtu.be/7qJYI5r98r8?si=v1oz6AQYTt4i-OEl

Tasken eOffice – Giải pháp tiên phong cho doanh nghiệp số

Điểm đột phá tạo nên sự khác biệt

Tasken eOffice không chỉ là một phần mềm quản lý thông thường. Đây là giải pháp quản trị doanh nghiệp toàn diện, được xây dựng trên nền tảng Microsoft với mục tiêu xóa bỏ rào cản về không gian và thời gian. Điểm đột phá của Tasken eOffice nằm ở khả năng tích hợp trực tiếp trên nền tảng Microsoft, tận dụng tối đa hệ sinh thái Microsoft. Sản phẩm cung cấp một nền tảng linh hoạt, cho phép doanh nghiệp tùy biến sâu theo đặc thù vận hành của mình.

Những tính năng vượt trội

Trợ lý ảo AI thông minh

• Hỗ trợ bộ phận bán hàng: quản lý dữ liệu khách hàng, đề xuất phương án tiếp cận
• Tối ưu nhân sự: theo dõi quy trình tuyển dụng, tự động nhắc việc
• Tạo ra hiệu suất vượt trội cho toàn bộ tổ chức

Chi tiết: https://youtu.be/IUHy07iweuU?si=rzc5QJSYdwGc5dfZ

Khả năng tích hợp không giới hạn

• Kết nối liền mạch với các phần mềm khác như iBanking
• Đồng bộ dữ liệu giữa các phòng ban qua API
• Mở rộng và tùy biến theo nhu cầu thực tế

Trải nghiệm làm việc hiện đại

• Không còn chồng chất giấy tờ, không còn phê duyệt thủ công
• Quản lý công việc, phê duyệt văn bản, ký số điện tử trong một giao diện duy nhất
• Truy cập mọi lúc, mọi nơi trên mọi thiết bị

Hành trình phát triển đầy cảm hứng

Từ ý tưởng đến hiện thực

Phóng sự đã tái hiện chân thực hành trình từ những ý tưởng ban đầu đến sản phẩm hoàn thiện. Mỗi dòng code, mỗi đêm thức trắng, mỗi ý tưởng táo bạo của đội ngũ Opus Solution đều dẫn đến khoảnh khắc vinh quang này.

Tasken eOffice kiến tạo một môi trường làm việc số hóa, nơi mọi quy trình được chuẩn hóa và tự động. Từ đó, doanh nghiệp có thể vận hành hiệu quả, kiểm soát công việc minh bạch và đưa ra quyết định nhanh chóng.

Tinh thần đồng đội và khát vọng vươn xa

Video không chỉ tôn vinh thành tựu mà còn khắc họa rõ nét tinh thần đồng đội, sự đam mê và khát vọng vươn xa của đội ngũ phát triển. Những cảnh quay tại văn phòng cho thấy không khí làm việc sôi động, nơi mọi thành viên cùng nhau hướng tới mục tiêu chung.

Tầm nhìn tương lai đầy tham vọng

Hướng tới nền tảng quản trị thông minh

Đội ngũ Opus Solution không dừng lại ở thành tựu hiện tại. Họ đang tập trung phát triển Tasken trở thành một nền tảng quản trị doanh nghiệp thông minh, lấy AI làm trung tâm.

Những nâng cấp được kỳ vọng:

• Tự động đề xuất phương án xử lý công việc
• Dự báo các rủi ro tiềm ẩn trong vận hành
• Cá nhân hóa trải nghiệm cho từng người dùng
• Mở rộng sang lĩnh vực tài chính – kế toán

Xây dựng văn phòng ảo thực thụ

Tầm nhìn dài hạn là từng bước xây dựng một văn phòng ảo thực thụ, nơi mọi hoạt động đều có AI đồng hành – hỗ trợ ra quyết định nhanh hơn, chính xác hơn, và hiệu quả hơn.

Ý nghĩa của việc vinh danh

Khẳng định vị thế công nghệ Việt

Giải thưởng 5 sao tại Sao Khuê 2025 không chỉ là sự ghi nhận dành cho Tasken eOffice mà còn là minh chứng cho sức mạnh của công nghệ Việt Nam. Đây là động lực to lớn để các doanh nghiệp công nghệ trong nước tiếp tục đổi mới và phát triển.

Tạo động lực cho cộng đồng công nghệ

Thành công của Tasken eOffice góp phần nâng tầm công nghệ Việt trên bản đồ quốc tế, đồng thời truyền cảm hứng cho thế hệ nhà phát triển trẻ. Câu chuyện này chứng minh rằng với đam mê, tài năng và sự kiên trì, công nghệ Việt hoàn toàn có thể cạnh tranh và dẫn đầu trên thị trường toàn cầu.

Kết luận: Tự hào dấu ấn công nghệ Việt

Hành trình của Tasken eOffice từ ý tưởng đến vinh danh tại Sao Khuê 2025 là câu chuyện đáng tự hào về tinh thần đổi mới sáng tạo của công nghệ Việt Nam. Sản phẩm đã chứng minh sức mạnh của công nghệ trong việc tối ưu hóa quy trình, nâng cao hiệu suất và tạo ra giá trị bền vững cho doanh nghiệp.

Cảm ơn Giải thưởng Sao Khuê đã tạo sân chơi để các giải pháp công nghệ xuất sắc được tỏa sáng. Và cảm ơn đội ngũ Opus Solution đã mang đến một sản phẩm thực sự “Made in Vietnam” với chất lượng quốc tế. Chúng tôi tự hào mang đến các giải pháp chuyển đổi số toàn diện, giúp doanh nghiệp nâng cao năng lực cạnh tranh trong kỷ nguyên số:

• Giải pháp SharePoint: Là một SharePoint Company Vietnam hàng đầu, chúng tôi chuyên xây dựng hệ thống SharePoint Intranet và SharePoint Portal mạnh mẽ, cung cấp một SharePoint Solution tối ưu cho mọi doanh nghiệp.
• Văn phòng điện tử eOffice: Tự động hóa quy trình với Tasken eOffice. Trải nghiệm eOffice demo để khám phá giải pháp eOffice Solution hàng đầu Việt Nam.
• Nền tảng Microsoft: Tối ưu hiệu suất với Microsoft365 Vietnam và khai phá sức mạnh đám mây với các Azure solutions được tư vấn bởi đội ngũ Azure consultant in Vietnam chuyên nghiệp.
• Phân tích dữ liệu & Tư vấn: Đưa ra quyết định chiến lược với dịch vụ Vietnam Power BI Consultant service và nhận tư vấn chuyên sâu từ đội ngũ Vietnam business consultant giàu kinh nghiệm của chúng tôi.

The post Hành trình rực rỡ của Tasken tại Giải thưởng Sao Khuê 2025 appeared first on Opus Solution.

Each word in an AI prompt is broken down into clusters of numbers called “token IDs” and sent to massive data centers — some larger than football fields — powered by coal or natural gas plants. There, stacks of large computers generate responses through dozens of rapid calculations.

The whole process can take up to 10 times more energy to complete than a regular Google search, according to a frequently cited estimation by the Electric Power Research Institute.

So, for each prompt you give AI, what’s the damage? To find out, researchers in Germany tested 14 large language model (LLM) AI systems by asking them both free-response and multiple-choice questions. Complex questions produced up to six times more carbon dioxide emissions than questions with concise answers.

In addition, “smarter” LLMs with more reasoning abilities produced up to 50 times more carbon emissions than simpler systems to answer the same question, the study reported.

“This shows us the tradeoff between energy consumption and the accuracy of model performance,” said Maximilian Dauner, a doctoral student at Hochschule München University of Applied Sciences and first author of the Frontiers in Communication study published Wednesday.

Typically, these smarter, more energy intensive LLMs have tens of billions more parameters — the biases used for processing token IDs — than smaller, more concise models.

“You can think of it like a neural network in the brain. The more neuron connections, the more thinking you can do to answer a question,” Dauner said.

What you can do to reduce your carbon footprint

Complex questions require more energy in part because of the lengthy explanations many AI models are trained to provide, Dauner said. If you ask an AI chatbot to solve an algebra question for you, it may take you through the steps it took to find the answer, he said.

“AI expends a lot of energy being polite, especially if the user is polite, saying ‘please’ and ‘thank you,’” Dauner explained. “But this just makes their responses even longer, expending more energy to generate each word.”

For this reason, Dauner suggests users be more straightforward when communicating with AI models. Specify the length of the answer you want and limit it to one or two sentences, or say you don’t need an explanation at all.

Most important, Dauner’s study highlights that not all AI models are created equally, said Sasha Luccioni, the climate lead at AI company Hugging Face, in an email. Users looking to reduce their carbon footprint can be more intentional about which model they chose for which task.

“Task-specific models are often much smaller and more efficient, and just as good at any context-specific task,” Luccioni explained.

If you are a software engineer who solves complex coding problems every day, an AI model suited for coding may be necessary. But for the average high school student who wants help with homework, relying on powerful AI tools is like using a nuclear-powered digital calculator.

Even within the same AI company, different model offerings can vary in their reasoning power, so research what capabilities best suit your needs, Dauner said.

When possible, Luccioni recommends going back to basic sources — online encyclopedias and phone calculators — to accomplish simple tasks.

Why it’s hard to measure AI’s environmental impact

Putting a number on the environmental impact of AI has proved challenging.

The post Your AI use could have a hidden environmental cost appeared first on Opus Solution.

1. The Core of AI Technologies AI encompasses a broad range of technologies, including machine learning (ML), deep learning, natural language processing (NLP), computer vision, and robotics.

These technologies enable machines to learn from data, recognize patterns, make decisions, and even understand human language. The development of large language models (LLMs) and generative AI (like ChatGPT, DALL-E, and similar models) represents a significant leap forward, enabling more natural interactions between humans and machines.

2. AI in Business Applications AI is revolutionizing business operations across various domains:

Customer Service: AI-powered chatbots and virtual assistants provide 24/7 support, answer FAQs, and handle routine inquiries, improving response time and customer satisfaction.

Marketing and Sales: Predictive analytics helps businesses understand customer behavior, segment audiences, and create personalized marketing campaigns.

Finance: AI automates fraud detection, credit scoring, and algorithmic trading, increasing efficiency and reducing risks.

Manufacturing: Smart factories use AI for predictive maintenance, quality control, and supply chain optimization.

Healthcare: AI assists in diagnostics, drug discovery, and personalized treatment planning.

3. AI and Data:

The Fuel Behind Intelligence AI systems rely heavily on data to function effectively. Big Data, cloud computing, and IoT (Internet of Things) have created an ecosystem where vast amounts of data are constantly generated. AI processes this data to deliver actionable insights, identify trends, and support real-time decision-making. However, managing data quality, privacy, and security remains a key challenge.

4. Ethical and Societal Considerations The rapid advancement of AI brings several ethical and societal questions:

Bias and Fairness: AI models can perpetuate biases present in training data, leading to unfair outcomes in areas such as hiring or lending.

Job Displacement: Automation may render certain jobs obsolete, requiring workforce reskilling and government intervention.

Privacy Concerns: The collection and use of personal data raise significant privacy issues.

Accountability: As AI makes more decisions independently, assigning responsibility for errors becomes complex.

Responsible AI development emphasizes transparency, inclusivity, and governance frameworks to mitigate these risks.

5. Future Trends and Opportunities Looking ahead, AI will continue to evolve and integrate more deeply into every aspect of life and work:

Edge AI: Bringing AI processing closer to devices and sensors for faster response and lower latency.

Explainable AI (XAI): Developing systems that can explain their decision-making processes to build trust.

AI in Sustainability: Optimizing energy use, reducing waste, and tackling climate change through smart systems.

Collaboration with Humans: AI as a co-pilot in creative, strategic, and analytical tasks.

Conclusion Artificial Intelligence stands as a transformative force with the power to drive unprecedented efficiency, creativity, and insight. For businesses, embracing AI is no longer optional but essential for staying competitive in a data-driven world. At the same time, a thoughtful approach to AI ethics and governance will be critical to ensuring that the technology benefits all of society.

As we stand at the intersection of possibility and responsibility, AI challenges us to reimagine what it means to innovate with purpose.

In the era of the Industrial Revolution, integrating technology into business operations is not only essential but also an inevitable trend. It helps reduce workload while maintaining efficiency and strengthening a company’s market competitiveness. Moreover, leveraging management software allows businesses to build a structured system, enhancing consistency, transparency, and accuracy in operations.

Tasken eOffice, developed by Opus Solution, a leading business consultant in Vietnam, serves as an internal work management platform. It simplifies operations by automating online approval processes, offering a user-friendly and efficient solution. By implementing Tasken eOffice, businesses can optimize their workflow and accelerate their digital transformation journey.

The post Artificial Intelligence: Transforming the Future of Business and Society appeared first on Opus Solution.

As a result, many AI projects fail. According to IDC, only abut 35% of organizations succeed in getting models into production successfully.

“While we see AI technologies performing a swath of incredible feats such as Google Translate, AlphaGo, and solving a rubik’s cube, it can be hard to tell which business problems AI is apt to solve,” said Ankur Goyal, who is the CEO at Impira. “This has led to a lot of confusion—and a vendor community that has taken advantage of it by labeling things as AI when they aren’t. It’s very reminiscent of early last decade when cloud technologies took off and we had a lot of cloud washing going on. We had vendors marketing themselves as cloud players when their offerings were vaporware. Similarly, we are going through a period of AI washing now.”

So then, if your company is thinking of implementing AI, what is the best way to start? How can you help boost the odds of success and avoid the pitfalls?

Here’s a look at some strategies:

Beware Of The Hype

AI is not magic. It will not solve all your company’s problems.  Rather, you need to take a realistic approach to the technology.

“Unlike traditional data analytics, machine learning (ML) models that power AI are not always going to offer clear-cut answers,” said Santiago Giraldo, who is the Senior Product Marketing Manager of Data Engineering at Cloudera. “Implementing AI into the business requires experimentation and an understanding that not every experiment is going to drive ROI. When an AI project is successful, it is often built on top of many failed data science experiments. Taking a portfolio approach to ML and AI enables greater longevity in projects and the ability to build on the successes more effectively in the future.”

Interestingly enough, there are often situations when the technology is really just overkill!

“Often times businesses take on AI projects not realizing that it might have been cheaper to continue a process manually instead of investing large amounts of time and money into building a system that doesn’t save the company time or money,” said Gus Walker, who is the Senior Director of Product Management at Veritone.

Governance First

You don’t want to spend time and money on a project and then realize there are legal or compliance restrictions. This could easily mean having to abandon the effort.

“First, customer data should not be used without permission,” said Debu Chatterjee, who is the senior director of platform AI engineering at ServiceNow. “Secondly, bias from data should be mitigated. Any model which is a black box and cannot be tested through APIs for bias should be avoided. The risk of bias is present in nearly any AI model, even in an algorithmic decision, regardless of whether the algorithm was learned from data or written by humans.”

Identify the Problem To Be Solved

In the early phases of an AI project, there should be lots of brainstorming. This should also involve a cross-section of people in the organization, which will help with buy-in. The goal is to identify a business problem to be solved.

“For many companies, the problem is that they start with a need for technology, and not with an actual business need,” said Colin Priest, who is the VP of AI Strategy at DataRobot. “It reminds me of this famous quote from Steve Jobs, ‘You’ve got to start with the customer experience and work backwards to the technology. You can’t start with the technology and try to figure out where you’re going to sell it.’”

The problem to be solved should also be specific–that is, something that can be measured–and narrow. Don’t boil the ocean.

“It is the small steps that count,” said Mike Brooks, who is the Senior Director of APM Consulting at Aspen Technology. “Do not make the mistake of trying to make AI work for everything, all at once. After analyzing value for each AI initiative, the real benefits come when it solves a very specific goal.”

Costs

While it is important to estimate the ROI of a project, there is often too little attention paid to the cost side of the equation. But this can lead to disappointment. After all, it is never fun to be over budget on a corporate initiative.

“Companies looking to implement an AI project should start by looking at the cost of the operation and doing an analysis on how that cost structure compares to best practices,” said Jerry Kurtz, who is the EVP and Head of I&D at Capgemini North America. “The cost of storing and transforming data is typically 70% of the budget, and only brings 10% of the value. Being able to leverage AI to solve business problems is only 30% of the cost, and brings 90% of the value. If an organization can reduce data costs and improve data quality, they’ll have more budget to put toward leveraging AI to solve those business problems, like improving productivity and efficiency.”

Buy-In

Implementing AI can be wrenching for an organization.  Employees may be skeptical of the technology and could fear for their jobs.

This is why there needs to be focus on getting buy-in, which means having clear communication of the benefits. It should also involve a commitment from the C-Suite. Consider that—according to a recent survey from O’Reilly—the biggest bottleneck for AI is an unsupportive culture.

“For AI to succeed you must have the buy-in of your workforce and the right employee upskilling programs,” said Anand Rao, who is the Global AI Lead at PwC. “You can’t simply offer AI training courses to employees; you need to go further and offer both immediate opportunities and incentives to apply what they’ve learned. Furthermore, business stakeholders and end-users—not just the tech staff—need to be included from the beginning of any project. If they’re not brought in at the start, your organization risks building a solution that does not work for the people who will be using it.”

Forbes

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 Before the world was even conscious of the threat posed by COVID-19, artificial intelligence had detected the beginnings of the outbreak.

On December 30, 2019, researchers from BlueDot, a company that uses AI to track and anticipate infectious diseases, spotted a report about a pneumonia of unknown aetiology in China.

Nearly a week later, on January 5, 2020, the World Health Organization (WHO) issued a request for more information from Chinese public health authorities. At the time, there had been 44 cases reported, with eleven patients in critical condition.

In the worldwide crisis that has since ensued, leaders from all sectors of the healthcare industry have turned to novel technologies to help monitor and control the spread of COVID-19 – the same technologies that were able to detect the initial outbreak.

Entities have leveraged artificial intelligence platforms, analytics algorithms, and data visualization tools to try to get ahead of the virus, or at least keep up with it. These technologies have the potential to anticipate where the disease will go next, as well as identify drugs that may be effective against COVID-19.

However, the issues surrounding AI in healthcare haven’t just disappeared in the wake of a global pandemic. Concerns about data quality, the absence of humans, and the overall accuracy of AI tools are perhaps even more valid now than they were before.

How are organizations currently using AI to combat COVID-19, and is the technology mature enough to handle a widespread health crisis?

AI’S CURRENT ROLE IN THE GLOBAL PANDEMIC

As the world begins to take more and more precautions to reduce the spread of COVID-19, organizations have sought to get a handle on the virus using innovative tools.

On March 5, 2020, Google’s DeepMind published research discussing how they used deep learning to predict the structure of proteins associated with SARS-CoV-2, the virus that causes COVID-19.

“We emphasize that these structure predictions have not been experimentally verified, but hope they may contribute to the scientific community’s interrogation of how the virus functions, and serve as a hypothesis generation platform for future experimental work in developing therapeutics,” DeepMind researchers said.

At the federal level, policymakers are seeking expertise from the tech community to uncover new insights about COVID-19. The White House Office of Science and Technology Policy recently issued a call to action for AI developers to build tools that can be applied to a new COVID-19 dataset.

Community-based organizations are also leveraging AI to reduce the impact of the disease. At Medical Home Network (MHN), a Chicago-based nonprofit, leaders have implemented an AI platform to identify Medicaid patients most at risk from COVID-19.

“Community preparedness is critical. The dedicated MHN care teams who proactively reach out to patients via phone, email or text will help individuals understand what they can do to lower their chance of infection, recognize symptoms of infection and how best to access the advice and care they need,” said Dr. Art Jones, Chief Medical Officer at Medical Home Network.

To track the spread of the virus, several entities have employed data visualization platforms. On January 22, 2020, Johns Hopkins University first shared an interactive dashboard that tracks real-time data on confirmed coronavirus cases, recoveries, and deaths.

Additionally, officials in Latin America have rolled out a platform that facilitates the collection, investigation, and visualization of case and contact data during an outbreak.

FEWER HUMANS COULD LEAD TO MORE PROBLEMS

Although AI has played a considerably big part in the COVID-19 pandemic so far, several concerns about the utility of the technology remain, particularly about its ability to function without human involvement.

With the US cracking down on social distancing, some major websites are turning to AI systems as they tell their employees to work from home – including Facebook, Twitter, and YouTube, BBC News reports. As a result, these tech giants are relying on automated algorithms to find and remove problematic material on their platforms, which the companies have admitted could lead to some mistakes.

While this development doesn’t directly apply to healthcare, it does raise some important questions about the ability of AI to stand on its own.

Patients, providers, and payers have previously expressed concerns about AI replacing doctors, but these anxieties have abated in recent years as most in the industry have come to view AI as tools that can augment physician practices.

In the case of COVID-19, it seems that experts are taking a similar stance.

In an article published in The Lancet Digital Health, David Heymann, Executive Director of the Communicable Diseases Cluster, WHO, noted that people working at outbreak sites can collect vital data on COVID-19, which could then be used to train AI models. This data includes transmissibility, risk factors, incubation period, and mortality rate.

“We can’t replace the human brain at this point, nor the epidemiologist or virologist with anything that can analyze and rapidly do what is necessary at the onset of an outbreak. We still need to prime that AI with information from study of the evidence and link this to events in the outbreak,” Heymann said.

THE ONGOING CONCERN OVER DATA QUALITY

In addition to a lack of human involvement, data quality issues are also top of mind for healthcare leaders seeking to use AI to combat COVID-19.

The industry’s persistent challenges with data volume, quality, and accuracy have been well-documented, especially as health systems move to adopt AI and analytics tools.

In the current climate, access to quality, accurate, and complete data will have a significant impact on whether AI tools will help or hinder COVID-19 prevention and treatment efforts. The number of confirmed cases increases daily around the world, as do the number of deaths. Keeping track of this information is as difficult as it is crucial, and leaders must make sure they’re training AI tools on the most up-to-date data available.

“Our data won’t necessarily come from China because it hasn’t been able to get hold of the data it needs because of the disorder and panic,” Heymann said in The Lancet article.

“This virus has spread to 24 other countries and these countries have set up extremely good systems of contact-tracing and patient isolation. This is where our information will come from.”

Moritz Kraemer, a spatial epidemiologist at the University of Oxford in the UK, told The Lancet that his team receives news reports and government reports twice per day that provide information on how many cases exist in any one location. Search engines and social media have also helped them track the disease.

“Before Chinese New Year we looked at how many people left Wuhan over a day, and this information comes from search engines. WeChat, a messaging, social media and mobile payment app, provides data on travel around Wuhan,” he said.

“Machine learning models use these data to predict the most likely location of where novel coronavirus might arrive next and this might inform where and how to run border checks.”

With all the uncertainty surrounding COVID-19 and the healthcare industry as a whole, it’s difficult to know how AI will ultimately fit into the pandemic. As the situation continues to evolve, so too will the technology. Whether it features large or small in the fight against this new threat remains to be seen.

Health IT Analytics

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When Oxford University Professor Nick Bostrom’s New York Times best-seller, Superintelligence: Paths, Dangers, Strategies was first published in 2014, it struck a nerve at the heart of this debate with its focus on all the things that could go wrong. However, in my recent conversation with Bostrom, he also acknowledged there’s an enormous upside to artificial intelligence technology.

Since the writing of Bostrom’s book in 2014, progress has been very rapid in artificial intelligence and machine and deep learning. Artificial intelligence is in the public discourse, and most governments have some sort of strategy or road map to address AI. In his book, he talked about AI being a little bit like children playing with a bomb that could go off at any time.

Bostrom explained, “There’s a mismatch between our level of maturity in terms of our wisdom, our ability to cooperate as a species on the one hand and on the other hand our instrumental ability to use technology to make big changes in the world. It seems like we’ve grown stronger faster than we’ve grown wiser.”

There are all kinds of exciting AI tools and applications that are beginning to affect the economy in many ways. These shouldn’t be overshadowed by the overhype on the hypothetical future point where you get AIs with the same general learning and planning abilities that humans have as well as superintelligent machines. These are two different contexts that require attention.

Today, the more imminent threat isn’t from a superintelligence, but the useful—yet potentially dangerous—applications AI is used for presently.

How is AI dangerous?

If we focus on what’s possible today with AI, here are some of the potential negative impacts of artificial intelligence that we should consider and plan for:

Change the jobs humans do/job automation: AI will change the workplace and the jobs that humans do. Some jobs will be lost to AI technology, so humans will need to embrace the change and find new activities that will provide them the social and mental benefits their job provided.

Political, legal, and social ramifications: As Bostrom advises, rather than avoid pursuing AI innovation, “Our focus should be on putting ourselves in the best possible position so that when all the pieces fall into place, we’ve done our homework. We’ve developed scalable AI control methods, we’ve thought hard about the ethics and the governments, etc. And then proceed further and then hopefully have an extremely good outcome from that.” If our governments and business institutions don’t spend time now formulating rules, regulations, and responsibilities, there could be significant negative ramifications as AI continues to mature.

AI-enabled terrorism: Artificial intelligence will change the way conflicts are fought from autonomous drones, robotic swarms, and remote and nanorobot attacks. In addition to being concerned with a nuclear arms race, we’ll need to monitor the global autonomous weapons race.

Social manipulation and AI bias: So far, AI is still at risk for being biased by the humans that build it. If there is bias in the data sets the AI is trained from, that bias will affect AI action. In the wrong hands, AI can be used, as it was in the 2016 U.S. presidential election, for social manipulation and to amplify misinformation.

AI surveillance: AI’s face recognition capabilities give us conveniences such as being able to unlock phones and gain access to a building without keys, but it also launched what many civil liberties groups believe is alarming surveillance of the public. In China and other countries, the police and government are invading public privacy by using face recognition technology. Bostrom explains that AI’s ability to monitor the global information systems from surveillance data, cameras, and mining social network communication has great potential for good and for bad.

Deepfakes: AI technology makes it very easy to create “fake” videos of real people. These can be used without an individual’s permission to spread fake news, create porn in a person’s likeness who actually isn’t acting in it, and more to not only damage an individual’s reputation but livelihood. The technology is getting so good the possibility for people to be duped by it is high.

As Nick Bostrom explained, “The biggest threat is the longer-term problem introducing something radical that’s super intelligent and failing to align it with human values and intentions. This is a big technical problem. We’d succeed at solving the capability problem before we succeed at solving the safety and alignment problem.”

Today, Nick describes himself as a “frightful optimist” that is very excited about what AI can do if we get it right. He said, “The near-term effects are just overwhelmingly positive. The longer-term effect is more of an open question and is very hard to predict. If we do our homework and the more we get our act together as a world and a species in whatever time we have available, the better we are prepared for this, the better the odds for a favorable outcome. In that case, it could be extremely favorable.”

Forbes

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Planning projects accurately is notoriously difficult, whether they’re publicly or privately funded, or in domains like construction, technology, pharma, or infrastructure. According to the 2018 “Pulse of the Profession” study conducted by the Project Management Institute, between 2011 to 2018 only about 50% of projects where completed on time and approximately 55% were within budget. Even though firms have been investing in project management techniques since the 1970s, the accuracy of project plans has not improved much.

Inaccurate forecasts involving durations, costs, resources, and benefits are clearly major source of risk for leaders’ careers and organizations’ growth opportunities. For example, firms waste an average of $119 million for every $1 billion spent (11.9%) on projects due to poor project performance. Late or pricey projects can also affect the health of the economy at large. Gross domestic product (GDP) contributions from project-oriented industries are forecasted to reach $20.2 trillion by 2027; major missteps have the potential to chip away at this number.

Forty years ago, psychologist and Nobel prizewinner Daniel Kahneman, along with long-term collaborator Amos Tversky, noted that humans tend to suffer from a planning fallacy: they overpromise and underdeliver by offering unrealistic forecasts of projects’ objectives. Kahneman and Tversky suggested using an outside view to develop more realistic project plans. They proposed using a forecasting technique called reference class forecasting, by which projects’ durations or costs are predicted by comparing a project of interest to a set of past similar projects. Such as outside view is in contrast to the inside view that’s more often taken, where the project is planned with little regard to historical performance and its ability to meet set targets.

Today, changing attitudes toward data collection, data-driven prediction, and decision-making offers unprecedented opportunities in the field of project planning. With data, firms can now operationalize Kahneman and Tversky’s ideas, going beyond their original vision. Using historical data on projects’ initial forecasted completion dates and total costs, in addition to realized or actual expenditures and durations, accuracy estimates can be established. Such estimates can then be used when forecasting and setting new projects’ goals. Here are some examples of places where data-based prediction is occurring, as well as some publicly available resources you can use.

In the U.K., data on project performance has been collected for over a decade now. The HM Treasury’s Green Book provides guidance on how project proposals should be appraised before significant public funds are committed. The appraisal procedure includes an explicit adjustment to account for systematic optimism, sometimes referred to as “optimism bias,” which is the overstatement of benefits and the understatement of durations and costs.

These optimism adjustments use reference class forecasting and are constructed empirically based on historical data deemed relevant. In a study I conducted for the U.K.’s Department for Transport, along with researchers from University College London, Erasmus University Rotterdam, and Warwick Business School, we found that rail infrastructure projects require anywhere from a 64% optimism adjustment (for projects in early definition stages) to a 4% adjustment (for projects that have already completed detailed designs). These adjustments were determined by an analysis of data from thousands of historical projects.

Much of the Green Book’s guidance regarding optimism bias and required adjustments has been inspired by work done by Oxford professor Bent Flyvbjerg. Professor Flyvbjerg has collected data on hundreds of large-scale projects, mostly in the areas of infrastructure, construction, and information technology. Subsets of projects in this dataset serve as relevant reference points when appraising new initiatives.

In the U.S., the Program Management Improvement Accountability Act was signed into law on December 14, 2016. The Act, which aims to improve program and project management practices within the Federal Government, establishes initial guidance for coordinated and government-wide approaches to strengthen project management practices. Aimed at federal agencies, the goal is to improve government performance, including the “use of cost and schedule data to support decision-making.” In the future, this Act will hopefully serve as a significant catalyst to help establish resources for data collection. The fact that the government took this step could also help to establish new norms in project-related data collection.

In both of these examples, the set of projects that are considered in the reference class is identified by human judgement. What if artificial intelligence could help perform this role? Data availability and access to data-driven artificial intelligent algorithms can help take reference class forecasting to the next level, revolutionizing how projects are being planned and how targets are being set.

How? Using detailed project-plan-level data with information on individual tasks, deep learning and artificial intelligence can identify patterns of similarity among project tasks, hierarchies, and precedent relations. nPlan, a London-based startup, is doing this now. The company uses data from tens of thousands of construction projects involving millions of tasks combined with natural language processing techniques to predict project durations and delays. The combination of rich data and proprietary AI capabilities allows nPlan to generate highly accurate and useful forecasts for project completions dates, including information about the risks of delay. Here, AI algorithms learn which patterns are most useful for predicting delays, relaxing the need to declare a reference class upfront.

There have been those who suggest that the availability of data and AI technologies will introduce a “seismic shift” in project planning. Let’s hope that this shift will finally enable us to overcome the planning fallacy, too.

Yael Grushka-Cockayne @ Harvard Business Review

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A Microsoft survey of more than 400 senior executives across eight global markets found that executives expect AI to benefit their businesses to positively impact growth, productivity, innovation, and job creation. A stunning 94% of them see AI as an important tool for solving strategic challenges.

But while the future is already here, as American fictionist William Gibson points out, it’s not evenly distributed. These five industries are paving the way forward with AI:

1. Finance

Nowhere has AI made a bigger splash than in the finance sector. Machine learning models distill insights from large volumes of quantitative data, making them an appealing tool for banks and day traders. Automated trading now accounts for about 75% of financial market volume, in no small part thanks to companies like Algoriz. Founded by Goldman Sachs and Millennium Partners alumni, Algoriz makes it possible for nontechnical users to construct trading algorithms.

Finance pros who work behind the scenes have embraced AI as well. Payment gateway FlexPay uses AI to predict and prevent credit card declines. By adapting several characteristics of a merchant’s transactions to better meet the risk standards of financial institutions, it lowers their risk score and significantly increases the ratio of approved payments. As AI matures, expect to see rules-based systems phased out for everything from fraud detection to equity management. 

2. Healthcare

Earlier this year, Google’s Deep Mind shocked the medical world by detecting lung cancer in CT scans better than six trained radiologists. The model reduced false positives by 11% and detected cancer 5% more often.

Startups are bringing AI’s detective capabilities to bear on other cancers as well. Health tech company CureMetrix’s model improves evaluation of dense breast tissue, helping radiologists reduce recall rates and spot cancer earlier.

Autonomous surgical robots aren’t likely to conduct procedures any time soon, but do expect AI to show up in back-office work. Medical billing and coding involves matching procedure data to specific patient accounts, the sort of rote task AI excels at.  

3. Entertainment and sports

While not exactly life and death, entertainment and sports nonetheless touch lives in meaningful ways. Tools like Bertie provide media sites, including Forbes, with first drafts and templates for common news events.

Not to be outdone, streaming sites like Spotify use machine learning to act as a personal DJ for millions of listeners. By looking at data points like vocals, tempo, and instrumentation, the audio services comes up with uncanny matches.

Professional sports are also being overhauled by AI. Basketball coaches are using algorithms to act as opposing team members during practice sessions. By showing players how their opponents might respond, coaches can explain how a certain move might fit into the game’s bigger picture.

4. Travel and transportation

AI is all about optimization. In major cities, that means making sure traffic flows as smoothly and with as few accidents as possible. Startups like Waycare are helping cities develop more efficient roadways by analyzing municipal traffic data. In the future, that data will be blended with information transmitted by vehicles using the roads as well.

AI is also making self-driving cars a reality. Although Tesla has yet to push self-driving software out to vehicles on the road, the technology itself is nearly done. AI will be the “brains” of autonomous vehicles, helping them differentiate between road markings and obstacles like pedestrians. The result will be fewer deaths, more efficient trips, and ultimately fewer emissions.    

5. Marketing

Already, more than half of U.S. CMOs are using AI to add personalized touches to their marketing campaigns. But AI can do so much more for marketers than swap out names and send “happy birthday” messages.

Take ad placements. GumGum helps brand marketers use computer vision technology to scan videos and images, intelligently showing them to consumers deemed likely respond to them by its algorithm. The adtech startup claims that 70% of Fortune 100 companies use its technology.

Artificial intelligence has already had a profound impact on the ways many industries do business every day. As the technology continues to advance, innovators will discover new applications. And soon enough, other industries will see their successes and want a piece of the action.

Forbes

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By dragging and dropping in your favorite images and the relevant text to a slide, PowerPoint Designer leverages A.I. to automatically suggest compelling themes, layouts, and designs for you to use. In addition, A.I. can be called upon to help presenters make big numbers in a slide more relatable, and artificial intelligence can also help coach you in becoming a better speaker. These new A.I. tools follow the recent addition of A.I.-based closed-captioning feature for PowerPoint.

“We noticed that people were spending too much time building presentations, too much time on slides, and they weren’t having the impact that they wanted,” Shawn Villaron, PowerPoint Partner Group Program Manager at Microsoft, said in a video briefing. “So we started with a hypothesis that we could actually do better — we could use artificial intelligence to find ways to make it easier for people to make highly impactful, highly effective presentations.”

BUILDING SMARTER SLIDES

Microsoft studied how people used PowerPoint, and the company discovered that most users start building a slide with a photo. By simply adding a feature to the slide, PowerPoint Designer will analyze the photo and “suggest seven or eight ways that the slide could be built,” Villaron said.

After the text and visuals are added to a blank slide, PowerPoint Design will display a “Design Ideas” panel on the right-hand side, showing the possible visual layouts. Designer also can create a photo mosaic with multiple photos on the same slide, suggest effective text layout, or even build a timeline if it detects a sequence of events on the slide.

“Over time, we add new capabilities around looking for iconography and adding iconography to your slide to increase the effectiveness increase the impact fullness of your slides,” Villaron added.

Not only does PowerPoint Designer help you create a visually compelling way to tell your story, but Microsoft claims that it could save most users at least 30 seconds per slide and as much as five minutes. Additionally, with the powerful A.I.-based slide suggestions, Microsoft stated that it has reached a major milestone with PowerPoint Designer with users having saved and kept more than 1 billion slides suggested by the A.I.-based tool.

For organizations using PowerPoint, Designer even works with branded templates, allowing users to create slides that adhere to visual identity guidelines.

MAKE NUMBERS GREAT AGAIN

In addition to being able to build visually pleasing slide layouts, Designer even understands context, which can help you tell your story in a more meaningful way. While it’s important, for example, to tell your audience that sea turtles have to travel 1,500 miles just to lay their eggs, such a large number can be lost without context. Fortunately, PowerPoint’s A.I. tool can help add some meaning to that many miles. Within the same “Design Ideas” pane, PowerPoint Designer can also add that this distance is “about half the width of the United States.

“Through the addition of perspective, Designer can help make these numerical slides more digestible and help presenters to more effectively convey their information,” Derek Johnson, a PowerPoint manager, said in a demo, noting that the relatable references help make the information easier to understand and increases retention. “You’ll see this kind of perspective show up on all sorts of measures ranging from weights to distances to financial figures, and more coming soon.”

A.I. will propose context for a variety of number types, including currency, measurements, and more. The feature will be able to adapt based on the measurement used and location. “So I was showing an example with miles, but if I was doing that somewhere else in the world outside of the United States, I would be getting more relevant suggestions to my locale,” Johnson added.

The A.I.-based PowerPoint Designer tool will be available for Office for the web and Office 365. Microsoft noted perpetual licenses of Office won’t get the A.I. tool.

INTELLIGENT COACHING

In addition to using artificial intelligence to build powerful slides to tell more impactful stories, PowerPoint is also using machine learning to give you coaching to deliver a more effective presentation. The Presenter Coach in PowerPoint not only analyzes your speed — how fast you’re talking — but it can be used to give you feedback.

“Based on academic research and field studies, we’ve integrated presentation best practices into Presenter Coach to help people give more effective presentations,” Johnson said.

For example, if you’re just reading the words on your slide verbatim, Presenter Coach will pop up and tell you to pay attention to originality. If you stutter and start using filler words — it will tell you to cut those words out.

The tool also recognizes context as well. If you say that “you’re the best man for the job,” Presenter Coach will appear and let you know that the phrasing may be culturally insensitive. At the end of the presentation, you’ll get a more detailed coaching report, which proposes detailed suggestions, like “best man for the job” could instead be phrased  as “best person for the job.”

“So now when I’m finished with my presentation, I’ll exit out and coach will pop up and give me this detailed report that also includes pacing information to let me know how fast or slow I was talking during the presentation,” Johnson said. “This report gives me a recap of the tips that I received during the session and lets me know where to focus.”

At launch, Presenter Coach will be available on PowerPoint for the web later this year, but it will eventually head to desktop and mobile clients for Office 365 subscribers. Microsoft said that you need an internet connection to use the feature.

Digitaltrends

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