Northeastern University joins AI-higher ed experiment
Northeastern University partnered with the company behind Claude to test how AI can be incorporated into the college curriculum.
The big picture: Northeastern is among three colleges globally that will test Anthropic's new learning tool under Claude for Higher Education, which aims to help teach users instead of offering shortcuts to an answer.
Driving the news: All Northeastern students, faculty and staff will get access to the premier version of Claude, including the new "learning mode" that's central to Claude for Education.
Instead of answering a prompt, Claude's "learning mode" asks users how they would answer a question and what proof they have to back it up.
Northeastern will host workshops next week for students, faculty and staff who want to learn to use AI in their fields of study.
Anthropic also announced partnerships with the London School of Economics and Champlain College, VentureBeat reported.
By the numbers: Northeastern will give premier Claude access to nearly 49,000 students across its 13 campuses.
Another 3,500 faculty and 4,900 staff members will also have access.
What they're saying: Javed Aslam, chief of AI at Northeastern, says the AI could help students create study guides, quizzes on course materials and other resources.
He also wants professors to take advantage of the new technology.
"Part of our mission as a university is to really rethink how it is that we do both teaching and learning in the presence of AI," Aslam tells Axios. "It's really on both those fronts."
Yes, but: That also means another 50,000 people may be using energy to generate text- or image-based responses, though it's still hard to tell exactly how much energy a session with an AI model typically uses.
Northeastern formed an AI working group that will analyze the best way to incorporate AI into the curriculum, which may also help inform how Claude for Education can improve higher education.
Zoom out: The partnership is the latest example of a college embracing AI.
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
TechCrunch
38 minutes ago
- TechCrunch
Scale AI confirms ‘significant' investment from Meta, says CEO Alexandr Wang is leaving
Data-labeling company Scale AI confirmed on Friday that it has received a 'significant' investment from Meta that values the startup at $29 billion. The startup also said its co-founder and CEO Alexandr Wang is stepping down from his role to join Meta and help the bigger company with its AI work. Reports indicate that Meta invested about $14.3 billion for a 49% stake in the startup, which produces and labels data that's used to train the large language models that underpin a significant portion of generative AI development. Meta confirmed the investment. 'Meta has finalized our strategic partnership and investment in Scale AI. As part of this, we will deepen the work we do together producing data for AI models and Alexandr Wang will join Meta to work on our superintelligence efforts. We will share more about this effort and the great people joining this team in the coming weeks,' a spokesperson for Meta told TechCrunch. Jason Droege, Scale's current chief strategy officer, will hop in as interim CEO. Scale AI noted that Meta's investment will be used to pay investors and shareholders, as well as to fuel growth. The company emphasized that it remains an independent entity. Wang will continue at the data-labeling firm as a director on its board. As my colleague Max Zeff wrote on Wednesday, this investment is a way for Meta to improve its AI efforts as its rivals Google, OpenAI and Anthropic rush ahead, and the social media company's own AI model releases trail the competition. Plus, according to data by SingalFire, the company lost 4.3% of its top talent to other AI labs last year. For the last several years, leading AI labs such as OpenAI have relied on Scale AI to produce and label data that's used to train models. In recent months, Scale AI and its data annotation competitors have started hiring highly skilled people, such as PhD scientists and senior software engineers, to generate high-quality data for frontier AI labs. Just last year, Scale AI raised $1 billion last year from investors including Amazon and Meta at a valuation of $13.8 billion.
Fast Company
39 minutes ago
- Fast Company
SMB survival requires cybersecurity transformation with AI
Cybersecurity is now one of the top three risks threatening small and medium-sized businesses (SMBs) in 2025, alongside inflation and economic concerns. Yet many SMBs remain unaware of the full scope of their vulnerability to modern cyberattacks. With AI-powered automation, cybercriminals can attack thousands of SMBs at once, exploiting weak defenses and businesses without dedicated security teams or advanced AI technology. However, a concerning 60% of SMBs remain dangerously underprepared by believing large corporations are more likely targets or being convinced their business has no cyber vulnerabilities at all. The cost of inaction due to misplaced confidence is devastating: nearly 55% say it would take less than $50,000 in financial losses from a cyberattack to force them out of business. One in five SMBs have declared bankruptcy or shut their doors permanently after a cyber incident. Survival requires a shift in strategy. SMBs must take control by using the same advanced AI tools attackers rely on to fight back. A NEW STRATEGY: 4 SIMPLE STEPS TO START With the right strategies, SMBs can protect their data, minimize vulnerabilities, and transform cybersecurity from a cost into an asset. Here's how: 1. Know Your Data Cybercriminals don't attack randomly. They target what's easiest and most profitable: customer payment data, financial records, employee credentials, and business-critical systems. SMBs need to know what data they have, where it's stored, and who has access. Begin with data mapping. Inventory where your data lives—cloud platforms, servers, employee devices, and third-party tools. Identify what's stored, how sensitive it is, and who needs access. From there: Apply role-based access controls Encrypt sensitive information Use multi-factor authentication (MFA) Back up critical data regularly Train teams on basic security hygiene These quick wins dramatically lower risk without blowing budget. 2. Take Care Of The Basics Most cyberattacks aren't successful because of advanced hacking techniques. They succeed because of basic security gaps. Eighty percent of SMBs recognize their vulnerabilities, yet many continue to make basic mistakes. For instance: 23% use memorable passwords, like pet or family member names. 22% don't have cybersecurity measures for internet-connected devices like mobile phones. 18% don't require regular software updates, leaving known vulnerabilities unpatched. 16% never back up their data. 14% don't require MFA for staff. These simple, low-cost steps immediately reduce an SMB's attack surface. MFA can prevent 99% of account takeover attacks. Regular software updates close security gaps cybercriminals actively scan for. And limiting administrator privileges—so only essential personnel can install new software or access sensitive systems—reduces the chance of malware spreading through the network. 3. Train Your Team Technology only goes so far. Seventeen percent of SMBs admit they don't train their employees on cybersecurity best practices, even though human error remains the root cause of most breaches. Falling for phishing emails, mishandling sensitive data, or using unauthorized apps unwittingly opens the door to attackers. Build a culture of security by: Offering short, regular training sessions tailored to each role. Teaching employees how to identify and report phishing and social engineering attempts. Reinforcing password hygiene and safe device usage. Employees can be your weakest link or your first line of defense. The difference is training. 4. Prepare For The Inevitable Even the strongest security defenses can't stop every attack, but knowing how to respond can limit the damage. A cyber incident response plan doesn't need to be complex—just clear and actionable: Document a step-by-step process for handling an attack: how to identify one, who to notify, how to contain damage, and how to restore operations. Assign roles in advance so employees aren't scrambling to act. Test response plans regularly through tabletop exercises or simulated scenarios. More than half of SMBs (53%) experienced Wi-Fi or network disruptions in the past year. Others faced phishing attacks (48%) or website downtime (45%). These are common and costly, but with preparation, they don't have to be fatal. HOW MSSPs UPLEVEL YOUR STRATEGY Even with the best security mindset and preventative measures, most SMBs don't have the time, expertise, or resources to keep up. Seventy-four percent self-manage their cybersecurity or rely on a family member or friend. That's where managed security service providers (MSSPs) come in. For SMBs that are stretched thin and can't afford full-time security professionals, MSSPs provide proactive monitoring, threat detection, and rapid response, thereby acting as an extension of SMBs' internal teams by working 24/7 to identify and mitigate risks before they turn into costly breaches. But despite the clear need, only 15% of SMBs outsource to an MSSP. Offloading these responsibilities frees up valuable time and allows SMBs to focus on growth, rather than security. AI: THE SMB EQUALIZER TO MAXIMIZE DEFENSE Just as cybercriminals use AI to automate attacks and scale operations, SMBs can use AI to fight back. AI-powered security tools help SMBs detect threats faster, respond more effectively, and offload manual security tasks. AI recognizes anomalies in real time. Unlike traditional security measures that rely on pre-programmed rules, AI continuously learns and adapts by flagging suspicious activity before attacks can escalate. For example, AI can detect an employee logging in from an unusual location or a sudden increase in file downloads—both signs of an impending breach. In fact, 55% of SMBs say AI will be most useful in identifying cybersecurity threats before they impact business operations. AI also automates routine security processes. From identifying phishing texts and emails (49%) to automating software updates (26%), AI-driven security reduces the workload on small IT teams and ensures critical security measures aren't overlooked. Instead of manually sifting through endless security alerts, AI prioritizes real threats by helping SMBs respond quickly and efficiently. SMBs that take a proactive approach by leveraging AI-powered security and expert support from MSSPs don't just reduce risk, they gain a strategic edge. In a landscape where 20% of SMBs won't survive a single attack, investing in cybersecurity isn't a luxury. It's a necessity.
Forbes
41 minutes ago
- Forbes
The Digital Revolution: Unlocking The Power Of Data In Life Sciences
Rajnish Nath, President of Manufacturing, Automotive, Aerospace & Defense, and Life Sciences at Capgemini Americas. The future of healthcare is here—and it's digital. From accelerating research to improving patient care, the integration of AI, data analytics and cutting-edge technologies is driving the industry into a new era where disruption is the norm. However, while digital transformation has the potential to unlock unprecedented value, it's not simply a matter of upgrading technology. It requires a fundamental shift in mindset, with a strong focus on establishing a solid data foundation to drive meaningful progress in a world that is increasingly driven by automation, prediction, and personalization. Data is the cornerstone of today's digital landscape, as it fuels these AI-enabled technologies to learn, adapt and deliver reliable results. As AI becomes more sophisticated, its effectiveness depends entirely on the quality and integrity of the data it's built on. Without strong data, even the most well-designed strategies and initiatives are built on unstable ground. Throughout my 28-year tenure with Capgemini, I've served clients across all sectors and functions. A solid foundation of good data is particularly relevant in the life sciences industry, where advanced technology is bringing us into a new era of R&D and patient care. Our ability to understand how we leverage the power of data has never been more critical to achieving life-changing impact. The life sciences industry generates vast amounts of data, but collecting it alone isn't enough. The real value lies in transforming raw data into actionable insights that drive improvements across the entire value chain—from enhancing patient outcomes to streamlining business operations. Yet, only 37% of life sciences organizations have standardized frameworks and tools in place to effectively collect, analyze and manage the full spectrum of data, including patient records, real-world outcomes and clinical trial results. As the role of data in driving innovation becomes more apparent, many organizations are turning to strategic partnerships to streamline data management and deliver measurable business results. This shift comes at a time when the global life sciences analytics market is projected to nearly double—from $13.78 billion in 2025 to $27.75 billion in 2032—reflecting the growing demand for data-driven solutions. And while life sciences leads the charge, the value of quality data management is gaining importance across all industries, becoming the basis of digital transformation and a key factor in maintaining competitive advantage. Partnerships that bring together functional innovation and precise data integration have the power to be truly transformative. This combination lays the groundwork for scalable, future-ready solutions designed to meet the evolving demands of a digital-first world. After all, it's a saying we know well: Not all data is good data. In an age where information is everywhere, success depends not just on access but on the quality, accuracy and usability of the data at hand. At this stage of mainstream digital transformation, quality data and advanced technologies like AI, machine learning and robotics are deeply interconnected—you simply can't have one without the other. The push to integrate advanced technologies, particularly within software-driven solutions, is motivated by the need to improve R&D efficiency, enhance market intelligence, streamline operations and deliver more personalized, effective patient care. Despite the momentum, and with the global AI market in life sciences projected to reach $14.20 billion by 2034, many organizations still face considerable hurdles. Challenges related to strategy, governance, funding, talent, technology integration, data management and regulatory compliance often slow or complicate progress. In particular, the growing reliance on big data in drug discovery, development and clinical trials is forcing companies to reevaluate their capabilities. Because of these challenges and the growing importance of data management and advanced technologies, here are several best practices that organizations can implement to successfully navigate this evolving landscape. • Data-First Approach: Start AI initiatives by focusing on data quality and readiness to prevent cost overruns and delays and ensure AI readiness. Prioritizing data aligns transformation with business goals. • Leverage Expertise: Utilize proven expertise and methodologies to handle complex data challenges. This includes employing a repeatable data migration approach that minimizes the risk of unsuccessful implementations and ensures high-quality data that aligns with business goals. • Service Excellence: Engage in long-term guidance and services that extend beyond a project's onset. This includes consulting expertise, oversight, on-demand support and deep technical expertise to drive real business value continuously. • Data Competency: Build the practice around the solutions to ensure the data initiatives are aligned with business goals, deliver measurable ROI and meet key performance indicators. • Customer-Centric Focus: Prioritize the client's end-customer success by delivering exceptional value and aligning data outcomes with business outcomes. This approach minimizes risks and drives higher-value outcomes. In today's increasingly digital landscape, validated processes are no longer optional—they're essential in a highly regulated environment where certified systems ensure compliance and data integrity. Regulations such as medical device reporting (MDR) in the EU and unique device identification (UDI) in the U.S. have intensified the demand for accurate data collection and reporting, particularly as they relate to patient safety. At the same time, technological advancements are pushing organizations to continuously evolve their data models and adopt new systems to stay competitive. True effectiveness now lies in bridging regulatory compliance with business acumen, empowering organizations to make informed, scalable decisions backed by reliable data. Looking ahead, data management practices must become more intentional. Where companies once stored every piece of data indefinitely, there's now a clear need to purge outdated or irrelevant information. Retaining only business-critical data helps minimize risk, enhance accuracy and build cleaner, more actionable datasets. As digital transformation continues to reshape the life sciences industry, the ability to leverage quality data alongside advanced technologies will be the key to success. Organizations that build solid data foundations and integrate AI and machine learning effectively can drive faster innovation and better patient outcomes. With the right strategic approach, the future of healthcare is one where data is the driving force behind breakthrough treatments and operational excellence. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
