Information Week

Like the industrial revolution of the late 19th and early 20th centuries, today’s emerging bio revolution is based on a convergence of technologies, including computing, automation, and perhaps most critically, artificial intelligence.  Both artificial intelligence and biotech are exponential technologies, says Mike Bechtel, Deloitte Consulting’s chief futurist, in an email interview. “The convergence of AI and biotech creates a double exponential field,” he states. “Combined AI-fueled biotech has the ability to disrupt the drug discovery process, accelerate clinical trials, and better predict health outcomes for billions of people.”  Multiple Applications  AI is driving the bio revolution, and we’re seeing that impact across several key areas, says Sid Rao, CEO and co-founder of scientific computing services provider Positron Networks. AI is a game-changer, he states via email. “AI is being used to automate the creation of personalized agents for curing diseases.” He points to mRNA vaccines as an example. “When a patient’s cancer cells are sequenced, AI models [can] design the specific mRNA agents to target that patient’s tumor cells,” Rao says. “We’re talking about medicine tailor-made for individuals, made possible by AI.”  Rao notes that AI is also transforming drug discovery. “It’s determining which molecules can act as catalysts for critical biological pathways, identifying potential drug targets, or optimizing clinical trials,” he says. “With AI, we can predict patient responses or even simulate trial outcomes before they ever happen.”  Related:How CIOs Can Prepare for Generative AI in Network Operations Bechtel observes that AlphaFold, AI software developed by DeepMind, an Alphabet subsidiary, performs predictions of protein structure and “is saving trillions in drug research costs and yielding new breakthroughs with digital twins, allowing advanced protein structure prediction and design prior to physical synthesis.”  Other potential applications identified by Bechtel include:  Genome analysis: Genome sequencing costs have dropped from $14 million to about $1,000.  Clinical trials: InClinico is achieving 80% accuracy in predicting phase two and three trial successes, leading to more efficient trial processes. The firm utilizes massive amounts of data related to targets, diseases, clinical trials, and even scientists involved with the study at the preclinical and clinical stages.  Development speed and success: Pharmaceutical manufacturers leveraging AI have reduced drug development time by 40% and decreased drug failure rates by 70% through AI simulations and integrated processes.  Related:It Takes a Village: New Infrastructure Costs for AI — Utility Bills Predictive health: AI-powered analyses of health metrics can detect diseases before symptoms appear.  While emerging technologies can speed research while reducing costs, Bechtel notes that a growing number of current and potential adopters are starting to realize that AI can also help them perform current tasks better and more efficiently. “This elevation from efficiency to effectiveness stands to radically re-engineer historically tedious and time-consuming processes.”  Bechtel points to genetic sequencing and drug development as examples. “Both have historically required scarce specialized skills and expensive brute-force solutions,” he explains. “Given AI’s particular facility with pattern recognition and simulation, we’re accelerating today’s techniques and beginning to generate tomorrow’s altogether new approaches.”  Risky Business  AI-fueled biotech holds incredible promise for people, products, and the planet, but there are inherent risks that we need to be mindful, Bechtel says. Consider, for example, CRISPR-Cas9 technology. Its ability to genetically modify human embryos could one day eliminate inherited diseases, but it also raises serious ethical questions. The idea of “designer babies” and the unintended consequences that could be passed down through generations is something we have to approach with extreme caution. “We need ethical, perhaps even global, frameworks to guide how we might best navigate these breakthroughs.”  Related:Digital Mindset: The Secret to Bottom-Up GenAI Productivity The risks are many, says Tad Roselund, managing director and senior partner with the Boston Consulting Group. “For example, unequal distribution of benefits is a potentially major issue and particularly important here given the fact that we are dealing directly with things like extending lifespans, increasing resistance to diseases,” he observes in an online interview.  Leaving aside the potential of intentional misuse, companies must put the right governance, policies, guardrails, and controls in place, Roselund says. “Regulators’ expectations regarding this are clear, even if in many jurisdictions the details are still to be determined.”  There are also safety implications for both individuals as well as society as a whole, Roselund warns. “These technologies are modifying extremely complex and interconnected systems,” he notes, adding that the impact of an unintended failure could be significant. “This risk is exacerbated by the pace of development.”  Looking Forward  Rao predicts that the bio revolution will only achieve its full, transformative potential when every biologist has access to the knowledge, the infrastructure, the data, and the tools required to leverage AI. “When we close this gap — and we will — we’re talking about real, lasting benefits to society at large.”  source

It’s really getting a bit out of hand, isn’t it? Governments are vying for AI dominance with a desperation reminiscent of the nuclear arms race. The market is as moody as a teenager — a rabid fan of AI one second, and totally over it the next. Major enterprises cut staff and elected officials bend land use rules all as part of exciting “strategic AI investments.” American AI companies have invested hundreds of billions to build AI tools, while a Chinese AI startup claims to have whipped one up in a few million. While some media companies are meeting AI giants with multimillion-dollar lawsuits, others are meeting them with multimillion-dollar partnerships. The Screen Actors Guild is fighting to prevent studios from making AI-generated versions of movie stars while movie stars are starring in ads for AI companies during Monday Night Football. AI can solve the knottiest challenges of the climate crisis, some say, but running AI may worsen the climate crisis. There are oodles of new AI-enabled cybersecurity tools on the market, which you will need, to defend against new AI-enabled cyberattacks. And despite this, despite all the bells and whistles, sturm und drang, many CIOs look at their own AI story and find it … a little boring. A bit slow and tedious, maybe. The same basic story line: behind schedule, over budget. Even if they see a positive return on their investment, the project might be a letdown. So, what is the real cost of AI? What’s the price tag CIOs have to pay in the short term and what’s the cost to their business — and to society — in the long-term? That’s a long question. So in this special report that we’ll roll out across three weeks, InformationWeek will delve into direct and ancillary costs of investing in AI. What are the various costs of developing AI internally versus hiring third-party resources, the impact on community, the environment through the drain on power, and regulatory enforcement on the technology. What will it cost an enterprise in real and social currency to pursue AI? And can we afford what it will take to deliver on AI’s promise? Week 1: The costs and the hidden costs. Video: What Is the Cost of AI: Examining the Cost of AI-Enabled Apps The path to realizing those AI expectations, however, comes with a variety of costs that are not all monetary — and could have surprising impacts on the world. Video: If Everyone Uses AI, How Can Organizations Differentiate? As AI saturates the market, what becomes of its competitive advantages? Does it become a basic, digital commodity in the background? Here’s what’s coming next: AI’s Hidden Cost: Will Data Preparation Break Your Budget? Infographic: Comparing Costs of LLM Providers Optimizing AI: What Do Companies Need to Focus On? It Takes a Village: New Infrastructure Costs for AI — Utility Bills Dissecting The Darker Side of AI What Types of Liabilities Are Emerging From AI?  Who’s Hurting from the AI Talent Shortage Week 2: The sudden demands for AI, particularly generative AI, have outpaced the world’s ability to supply it. How Bad is the AI Chip Shortage Now, and How Does That Impact the Price of Your AI Project The Long-Term Impact of the AI Market Crash of Summer 2024 Cooling AI: How Hard Is It To Keep Temps Down Why the Grid Can’t Support AI MAP: How Hot are AI Hotspots? Spotlight: Loudoun County, Va. Spotlight: Phoenix, Arizona Spotlight: Santa Clara County, California Just How Rare are the Rare Earth Metals We Need for AI? How Will Politics Limit or Complicate US Access to AI? Week 3: How can you use AI more efficiently and more effectively, to keep costs down and improve outcomes? Is a Small Language Model Better Than a LLM for You? How to Determine ROI for an AI Project. How to Make Your AI Project Greener, Without the Greenwashing How to Set a Realistic Budget for AI Research Projects Working on Truly Green AI source

A Pennsylvania healthcare system agreed to pay $65 million to patients who had their medical photographs and personal information posted on the internet after the provider declined to pay ransom demands from a threat actor in an attack last year. The $65 million settlement stands as a stark warning to businesses that protecting data is a critical task. Failing to do so will be expensive.   Today’s technology landscape makes it challenging for businesses to protect their data.   Lehigh Valley Health Network, a 13-hospital organization, received an ultimatum to pay up or have patient data plastered across the internet. LVHN declined to pay the ransom, and the threat actor kept their promise. They released over the internet personal medical records and undressed patient images taken for diagnostic purposes.   But Lehigh Valley Health Network was not alone. Businesses across the US face the same risks: from January to June 2024, there were an average of 14 reported ransomware attacks each day.    It is also becoming difficult for companies to pay their way out of a ransomware crisis as federal guidelines have made paying a ransomware threat actor more difficult. The Treasury Department’s Office of Foreign Assets Control (OFAC) released an advisory in 2021 that stated American companies that pay ransoms to threat actors on the Specially Designated Nationals and Blocked Persons List or in sanctioned jurisdictions may face civil penalties and liability imposed by the federal government.   Related:Tidal Wave of Trump Policy Changes Comes for the Tech Space In other words, giving into ransom demands may invite the federal government’s wrath. But refusing to pay may invite the wrong side in a lawsuit. Putting aside the rock-and-a-hard-place dilemma, many companies lack a plan for what to do when a ransomware attack hits.   Building an Incident Response Plan  Just as companies need to prepare for extreme weather events and supply chain disruptions resulting from them, similar forethought is necessary for dealing with a ransomware or cyberattack. How will the company identify the attack, what are the initial steps to take, who will lead the response team, what advisors will they call, and what will prevent further harm?  Cyber-attacks are tricky. It can be weeks or months before a company discovers a vulnerability exists, meaning that companies may already be behind the eight ball in responding when they discover the attack occurred.   But whether an attack has been percolating for minutes or months, the incident response plan provides a structure and creates systems for teams to respond quickly and effectively. The data exfiltration from a ransomware attack exposes companies’ vulnerabilities.  Related:What’s New (And Worrisome) in Quantum Security? The first step is always assessing the damage. The response team must evaluate the attack to identify its extent, which may require hiring a third-party cybersecurity company to forensically understand the breach and its implications.  Prisons, hospitals, utility companies, and other life-and-death service providers that find themselves under attack may require more urgent response capabilities. For most other companies without an immediate life safety issue, it may make more sense to take time to assess how long ago the attack occurred and what it will take to restore the systems.   Without this diligence, businesses put themselves further at risk; if they return too quickly to their systems’ backup capabilities without understanding the timeline of the attack, they may not know whether the breach infiltrated the backup system too. Restoring the network using an infected backup would not only fail to cure the attack, but it may also exacerbate the threat and increase the ransom demands. But without the capability to restore the system from backups, a company may have less options in dealing with a ransomware attack.   Related:Infogram Test Managing After an Attack  Between the third-party negotiators and insurance coverage, there may be a way to financially manage the attack. There are third-party providers that negotiate with ransomware threat actors, and some insurance companies cover for ransomware attacks.   For other victims, paying the ransom themselves may be the only way out. While doing so may come up against OFAC guidance, the federal government may limit liability for companies that cooperate with them. While there’s no guaranteed exit ramp or roadmap here, industry associations are working to create guidance for companies that find themselves stuck in this dilemma.   The bigger issue companies face post-attack is managing the fallout. In the US, each state manages data breach disclosure differently, so a company’s legal obligation and the liability may change depending on where they operate.   Ransoms are high, breach-related settlements are high, and the reputational damage is high. As a result, cyberattacks are becoming more expensive each year, and insuring against ransomware attacks has become more difficult.   Diligent data protection is the best defense companies have. Organizations that are cautious about how they collect and store data will have less risk than those that are lackadaisical. Companies that don’t risk falling susceptible to an ever-rising financial threat.  source

In every technology wave, we inevitably find enterprises in two distinct camps: leaders and laggards. Leaders, often courageous and curious, are early adopters, eager to embrace innovation and set pace for the market. Laggards, often cautious and conservative, are the rest of the field, content to wait until the hype subsides at the risk of falling further behind.  We witnessed this with the birth of the internet, the rise of cloud computing, and now the dawn of generative AI. As a data strategist for 30 years, I’ve seen plenty of these industry-defining shifts, particularly now in the area of compute. However, this use case is also the riskiest for businesses, and it reveals the starkest differences between leaders and laggards. Leveraging GenAI to create an image that results in a salmon filet jumping upstream is relatively harmless but having GenAI hallucinate financial numbers or medical treatments poses enormous risks.   In a recent report we collaborated on with MIT SMR Connections, eight out of 10 (83%) early adopters of GenAI for analytics believe they have a competitive advantage over the market, and nearly half (48%) anticipate an ROI of 100% or more in the next three years.  So, with these early adopters seemingly having an insurmountable head start, how can enterprises that have traditionally adopted a wait-and-see mindset ensure they are not permanently left behind?  Related:The Real Cost of AI: An InformationWeek Special Report Focus on the ‘Why,’ Not Just the ‘How’ There’s intense FOMO (fear of missing out) when it comes to new technology, and GenAI is no exception. For nearly two years, boards and executives have been bombarded with commentary and projections that exalt GenAI’s economic and operational impacts. McKinsey reported that GenAI could add upward of $4.4 trillion annually to the global economy in productivity and efficiencies, while business titan Jamie Dimon, CEO of JP Morgan Chase, told shareholders in a letter earlier this year that GenAI has the potential to rival some of humanity’s most consequential inventions.  This pressure often leads enterprises to invest in technology simply because it’s in vogue. We get so blinded by the shiny new “how” (the technology) that we lose sight of the “why” (the business value). It should never be technology for technology’s sake, but rather the ways the technology can be applied to drive meaningful change within the business that help reduce costs, improve efficiencies, or create more frictionless experiences. If you can’t articulate the why, then don’t be so quick to embrace the how.  Related:What Is the Cost of AI: Examining the Cost of AI-Enabled Apps Evolve Thinking and Processes, Not Just Tech  To properly wrangle GenAI you first need to tame your data. Unsurprisingly, a common characteristic of many early adopters is a modern, integrated, cloud-based data estate. On the other hand, for many laggards their data house more closely resembles a disorganized attic: messy, fragmented, and of varying value. While the technology to manage, govern, and secure this influx of information has advanced, it’s paramount that our practices and principles evolve at equal velocity to account for more data, faster data, and different types of data.  How enterprises rethink data management should also extend to the relationship between data and business teams, which have traditionally been deeply siloed. As Robert Garnett from Elevance Health shared with me on The Data Chief podcast, data teams are finally earning a seat at the corporate table and evolving from order-taker to true business partner.   As GenAI continues to lower the barrier to entry for data users, it will require Jobs-Wozniak-like collaboration between these two groups to ensure a unified and centralized data-AI-business strategy.  Develop AI Literacy by Committee, Literally  AI and GenAI are no longer obscure concepts plucked from the pages of a sci-fi script. The ability for workforces to manipulate this technology safely and responsibly, to understand its potential and limitations, and smell out inaccuracies or hallucinations in its output, will directly influence businesses’ health, reputation and bottom lines.   Related:If Everyone Uses AI, How Can Organizations Differentiate? The reality is we collectively aren’t doing enough to reskill and upskill our workforces. With only 5% of organizations actively focused on building their AI literacy skills at scale according to Accenture, enterprises must recognize that AI literacy — like data literacy before it — is now a life skill, not just a business one.  It’s critical that every enterprise, regardless of sector or market, establish a responsible AI committee to build a framework that lays out the expectations and guidelines each line of business can then apply uniformly across the company.  This committee should be comprised of representatives across the organization including technical, business, security and privacy stakeholders. Its responsibilities should include evaluating proposed large language models, establishing safeguards for proprietary data, designing standards to evaluate ROI, determining the best mix of proprietary and third-party solutions, and ensuring access to comprehensive training and skills development curriculum.  The distance between leaders and laggards has never been more pronounced in the era of GenAI, but it’s not too late. With a firm grasp on business value, investments in modern data management, deeply aligned teams, and a commitment to employee education, enterprises can ensure this GenAI race is transformational, not just the latest hype.  source

Much like the seismic arrival of the internet, artificial intelligence quickly became the technology most every organization has sought to leverage, and we are still in the early days. The path to realizing those AI expectations, however, comes with a variety of costs that are not all monetary — and could have surprising impacts on the world. This is the opening chapter of an InformationWeek special series of stories and video essays to explore some of the costs that can be incurred in our collective pursuit of AI. As we try to answer the core question, what is the cost of AI, let’s start small. Let’s look at some of the costs organizations may face when they seek to develop AI-enabled apps in-house. This is often a way for enterprises to make their first inroads into leveraging AI for their operations. This video features footage from The AI Summit New York, December 2024 and includes excerpts from the event with speeches and panel discussions that include New York Governor Kathy Hochul; Haley Massa, ML solutions engineer, Snorkel AI; and Romi Mahajan, CEO, Exofusion. The video also includes one-on-one interviews with Rakesh Malhotra, principal for digital and emerging technologies, EY; Jehangir Amjad, head of AI platform, Ikigai Labs; Ritika Gunnar, general manager for data & AI, IBM, and Gianpaolo Barozzi, VP, 3P chief innovation and technology officer, Cisco. Related:The Real Cost of AI: An InformationWeek Special Report source

DeepSeek, the China-based AI startup that upended US technology stocks Monday, said cyberattacks have disrupted services for its chatbot platform. And the company’s vulnerability raises concerns about users’ data security and use, experts say. DeepSeek caused Wall Street panic with the launch of its low cost, energy efficient language model as nations and companies compete to develop superior generative AI platforms. Users raced to experiment with the DeepSeek’s R1 model, dethroning ChatGPT from its No. 1 spot as a free app on Apple’s mobile devices. Nvidia, the world’s leading maker of high-powered AI chips suffered a staggering $593 billion market capitalization loss — a new single-day stock market loss record. The company’s wild ride continued Monday night as the company reported outages it said were the result of “large-scale malicious attacks,” disrupting services and limiting new registrations. Ilia Kolochenko, CEO at ImmuniWeb and adjunct professor of cybersecurity at Maryland’s Capital Technology University, says it may be too early to accept the company’s attack explanation. “It is not completely excluded that DeepSeek simply could not handle the legitimate user traffic due to insufficiently scalable IT infrastructure, while presenting this unforeseen outage as a cyberattack,” he says in an email message. He adds, “Most importantly, this incident indicates that while many corporations and investors are obsessed with the ballooning AI hype, we still fail to address foundational cybersecurity issues despite having access to allegedly super powerful GenAI technologies.” The Devil Is in the User Details Considering the potential breach, security experts also worry about DeepSeek’s access to users’ data, which under China’s strict AI regulations, must be shared with the government. “All AI models have the same risks that any other software has and should be treated the same way,” Mike Lieberman, CTO of software supply chain security firm Kusari, says in an email interview. “Generally, AI could have vulnerabilities or malicious behaviors injected … Assuming you’re running AI following reasonable security practices, e.g., sandboxing, the big concerns are that the model is biased or manipulated in some way to respond to prompts inaccurately or maliciously.” China’s access to potentially sensitive user information should be a top security concern, says Adrianus Warmenhoven, a cybersecurity expert at NordVPN. “DeepSeek’s privacy policy, which can be found in English, makes it clear: User data, including conversations and generated responses, is stored in servers on China,” Warmenhoven says in an email message. “This raises concerns because of the data collection outlined — ranging from user-shared information to data from external sources — which falls under the potential risks associated with storing such data in a jurisdiction with different privacy and security standards.” Warmenhoven says users need to be on guard: “To mitigate these risks, users should adopt a proactive approach to their cybersecurity. This includes scrutinizing the terms and conditions of any platform they engage with, understanding where their data is stored and who has access to it.” Optiv’s Jennifer Mahoney, advisory practice manager for data governance, privacy and protection, says, “As generative AI platforms from foreign adversaries enter the market, users should question the origin of the data used to rain these technologies… When a service is free, you become the product and your user data is valuable. Should an unregulated an unsecure technology suffer a cyberattack, you could become a victim of identity theft or social engineering.” The Risk to National Security China and the US have been locked in a strategic battle over AI dominance. The US, under the previous Biden administration, blocked China’s access to powerful AI chips. DeepSeek’s ability to create an AI chatbot comparable to the best US-produced GenAI models at a fraction of the cost and power could give the adversarial nation the upper hand as the countries race to develop artificial general intelligence (AGI). “AI and associated cloud compute are now a nation’s strategic asset,” Gunter Ollman, CTO at security firm Cobalt, tells InformationWeek in an email interview. “Its security is paramount and is increasing targeted by competing nations with the full cyber and physical resources they can muster. AI code/models are inherently more difficult to assess and preempt vulnerabilities …” Organizations should also be wary of using DeepSeek’s open-source technology, Ollman says. “Organizations building atop open-source AI should plan for a potential future bloodbath of vulnerabilities and exploits in the near future.” A popular GenAI tool could lure unsuspecting users to fall for adversarial nation-state propaganda. The definition of “backdoor attacks” that normally involve malicious code should be expanded to included malicious misinformation, Ollman says. “Backdoors may extend to political and social influence, such as a model’s answers modifying history … Perhaps country-led open-source AI models are the modern equivalent of religious missionaries of past centuries.” source

Thanks to the legalization of gambling-related activities in many parts of the world, the betting industry is booming. The field’s current market size is over a billion US dollars, according to Statista, including both on-site and online betting operations.  As the market flourishes, a growing number of betters are hoping that AI will help them beat the odds.  Playing the Odds  AI’s role in gambling is still relatively new, with operators only just beginning to explore its potential impact on backend systems and player platforms, says Yoel Zuckerberg, chief product officer at Soft2Bet, an online casino and sports book software provider. In an online interview, he notes that most industry players currently encounter AI only in limited forms within games, yet he believes that the technology’s role is likely to expand. “In the near future, AI is expected to play an increasingly central role in gaming platforms, enhancing personalization and engagement.”  AI’s strongest attribute lies in its ability to enhance personalization and interactivity, Zuckerberg says. “By tracking player behaviors, preferences, and patterns, AI can deliver tailored, bespoke gameplay,” he explains. “Integrating gamification elements, such as rewards and challenges, AI can also foster stronger customer loyalty and engagement.”  Related:Speech-to-Speech AI: Empowering a More Connected World Bettors can turn to AI to uncover patterns that provide valuable insights for making informed decisions, says Marin Cristian-Ovidiu, CEO of Online Games.IO in an online interview. However, when it comes to games of pure chance such as slots or roulette, AI has little to offer, since those outcomes are completely random.  Many gambling operators are understandably wary of AI, worried that the technology could soon shift the balance of player engagement and strategy, Cristian-Ovidiu says. For gamblers eager to explore AI’s gambling potential, he recommends starting, and becoming familiar with, data analytics platforms.  AI is rapidly transforming the gambling industry, offering both opportunities and challenges, says Christian Nzouatoum, founder of Nzouat, a firm specializing in small business AI and software architecture. “In areas like sports betting, poker, and blackjack, AI can be a powerful tool for gamblers, allowing them to analyze massive datasets and make informed decisions based on predictive models,” he observes via email. “For example, in sports betting, AI can process player statistics, team dynamics, and even external factors like weather conditions, to offer insights that go beyond what a human could easily calculate.” In poker, Nzouatoum notes, “AI tools can assess an opponent’s behavior and adjust strategies accordingly.”  Related:Exploring the Positive Impacts of AI for Social Equity On the flip side, AI has only limited applicability in games of pure chance, such as slot machines and roulette, where outcomes are entirely unpredictable, Zuckerberg says. “However, it can still add value by personalizing the player experience, customizing rewards, and creating engagement-enhancing features within virtual slots and similar games.”  Other Concerns  Gambling organizations, including casinos and sportsbooks, are keeping a close eye on AI developments. “They understand the advantages but are also concerned about maintaining fair play and game integrity,” says video game blogger Dane Nk, in an online interview. For individuals looking to dive into AI-assisted gambling, Nk suggests starting with data analysis tools geared toward betters, of which there are many. “They can offer valuable insights but remember that the human touch — skills and game knowledge — should never be overlooked.”  Since AI’s regulatory framework remains largely undefined, with many jurisdictions lacking specific guidelines for its use, businesses — including gambling operators — are currently operating in a gray area. “To mitigate the risks, companies should stay informed on regulatory developments and strengthen internal policies to ensure compliance,” Zuckerberg advises.  Related:China’s DeepSeek Dethrones ChatGPT as US Tech Stocks Plunge Gambling Addiction Detection   Recent studies reveal a complex outlook in which AI is both a potential savior and a cunning manipulator in the world of gambling and addiction, says Christian Perry, CEO of Undetectable AI, a firm offering AI detection and conversion technology.  The key is balance and responsible use, Perry states in an email interview. He believes that casinos can, and should, identify problematic gamblers using AI, and take every possible measure to prevent exploiting them in any way. “In person and online casinos should acknowledge the benefits and risks of using AI,” he says.  Betting on the Future  AI is transforming not only the player experience but also gambling enterprises’ operational efficiency and strategic approach. “As we move forward, we anticipate that AI will play an integral role in shaping a more responsible, player-centric gaming environment,” Zuckerberg says. “It’s essential for [gambling] organizations to prioritize ethical AI practices, stay updated on regulations, and maintain a strong focus on transparency.”  source

Within his first week in office, President Trump signed a flood of wide-ranging executive orders and took actions that have significant implications for the technology industry. The sheer volume of change, along with the freezing and unfreezing of federal funding, sparked much confusion.   What are some of the biggest tech policy changes coming from the current administration, and what could they mean for the industry?   A New AI Order   Trump voiced plans to repeal Biden’s executive order on AI, arguing that it stifles innovation, and swiftly followed through. He signed an executive order — Removing Barriers to American Leadership in Artificial Intelligence — and also announced plans for Stargate, a $500 billion AI infrastructure initiative.  “The Stargate initiative is interesting as it aligns several large players in the space into a single entity to help push initiatives,” Max Shier, vice president and CISO at Optiv, a cybersecurity advisory services company, tells InformationWeek via email.   While those moves have big possibilities for the AI space, it will likely take time to see the effects.   “Most of the executives that I’m talking to now don’t feel like there’s a huge impact, at least right now.  And they’re continuing to make investments and pursue solutions as they were … in Q4 or second half of last year, and in fact investing, even more in those solutions from an AI perspective,” Bill Farmer, lead of the aerospace, defense, and government services investment banking team and managing director at Brown Gibbons Lang & Company, an investment bank and financial advisory firm, tells InformationWeek.   Related:What’s New (And Worrisome) in Quantum Security? With Chinese startup DeepSeek making strides, competition in the global race for AI market dominance is heating up. But there are still concerns over risk in the AI space. Several industry and consumer groups signed a letter calling for the White House to retain AI testing and transparency rules, CNBC reports.   “The removal of guardrails and oversight can be negative if tech companies are allowed to do whatever they want without ethical considerations guiding their conscience,” says Shier.   Cybersecurity Changes  The Trump administration fired the Cybersecurity and Infrastructure Security Agency’s (CISA) Cyber Safety Review Board (CSRB). The CSRB was investigating China-state backed APT group Salt Typhoon, the group responsible for a massive breach of US telecom companies.   “I could also see potentially a pullback in CISA’s authority and role. Their budget has been increasing year over year. At a minimum, I think they’re going to take a hard look at what those programs are and what that spending looks like,” says Deniece Peterson, senior director of federal market analysis at Deltek, an enterprise software and information solutions company.   Related:Data Thefts: Indecent Exposure to Risk During his campaign, Trump was vocal about his intentions to be tough on China, but he seems to be taking a more nuanced approach now that he has taken office, AP News reports.  What that means for the federal government’s approach to cyber threats from China remains unclear.   Peterson points out that Trump has established the President’s Council of Advisors on Science and Technology (PCAST). “That may incorporate some of those [cybersecurity] activities. We just don’t know yet,” she says.   DOGE and Government Spending  The Department of Government Efficiency (DOGE) is going to focus on “modernizing federal technology and software to maximize efficiency and productivity,” according to the executive order establishing the new department.   “Particularly the government services folks are extremely nervous about DOGE … [it is] looking at reducing government spending, looking at reducing services, looking at reducing contractors,” Farmer points out.   Related:Infogram Test Trump has also spoken about rescoping and even eliminating entire federal departments. How that will actually play out under this administration remains to be seen, but it could result in workforce reductions.   “The Trump administration is going to be looking at automation of certain functions,” says Peterson.   Workforce reductions and increased automation could mean opportunities for IT companies to vie for government contracts. “IT contractors are looking at this is about how can they support this new kind of environment and shift,” Peterson adds.   A Step Back from Regulation  Trump signed an executive order placing a regulatory freeze on federal agencies. This administration has made clear its plans for deregulation.   “His moves are not unexpected. He was very clear on what he wanted to achieve in the tech space and that is less restrictions on tech companies and more innovation,” says Shier.  One potential result of a lighter-touch approach to regulation could be more M&A activity in the tech industry.  “I think a lot of deals were shelved in the last three or four years that had the potential to be significant transactions but because of the regulatory risk, folks decided not to pursue those,” says Farmer. “That’s changed now. At least optically, people feel like there’s a higher chance that deals could get through.”  Early Days  It is still early days for the second Trump administration. Many of his executive orders are facing legal pushback, and the impact of the president’s actions are not readily apparent in many cases.   “It’s hard to figure out how the executive orders to date impact spending because there’s been a lot of confusion. There’s … a lack of clarity on what the scope is, what kind of spending these things apply to,” says Peterson.   Technology industry stakeholders will have to watch how these initial policy changes play out and prepare for the possibility of more.   “I will be watching whether they continue down the path of de-regulation and how it affects the use and consumption of tech, including AI and privacy,” says Shier.   In the cybersecurity space, the fate of the Cybersecurity Maturity Model Certification (CMMC) program is also something to watch. “CMMC has seen a significant amount of pushback by companies doing business with the government as they state it is too expensive to implement and would reduce the competition in the government contractor space,” says Shier.   Technology leaders will be keeping a close eye on federal tech policy changes

Debate continues over how artificial intelligence might upend current jobs and future careers, as nuances emerge in such discussions. The assumption that AI equals immediate job cuts to deliver efficiency might not be that simple, especially as more divisions within organizations and their leadership start to understand how they can leverage this technology. Certain jobs might be eliminated, yet other jobs could evolve with AI. This episode of DOS Won’t Hunt features Luke Behnke, vice president of product for Grammarly; Cliff Jurkiewicz, vice president of global strategy for Phenom; Ryan Bergstrom, chief product and technology officer for Paycor; Daniel Avancini, co-founder and chief data officer for Indicium; and Arun Varadarajan, co-founder and chief commercial officer for Ascendion. They discussed how AI already changes staffing, what skillsets organizations want in an AI-powered world, fears about job loss, what this may mean for executives in the C-suite who need to get up to speed on AI, and when organizations can comfortably rely on AI to enhance their workforce. Listen to the full podcast here. source

From automating complex tasks to providing deep insights through data analysis, artificial intelligence has reshaped the way businesses operate and compete in a global marketplace. Yet, we are still in the early stages, with new AI advancements emerging regularly, each promising to push the boundaries of what’s possible.   One of the most recent advancements is in the development of speech-to-speech AI technology, which is set to facilitate and enhance communication on an unprecedented scale. By enabling real-time voice translation and voice-based interactions with AI agents, speech-to-speech AI is poised to break down language barriers, streamline operations, and foster a more connected global economy.   The Architecture of Speech AI and Advancements  The term “speech-to-speech” might suggest a direct conversion of spoken language, but the reality is a more complex, multi-layered process. Today’s speech AI systems operate through a sophisticated three-step workflow:  Speech-to-Text (STT): The process begins by capturing voice input, which is then transformed into mel-spectrograms — a visual representation of the sound’s frequency content over time. Advanced neural networks, such as those used in models like OpenAI’s Whisper, apply deep learning techniques to these spectrograms, enabling automatic speech recognition (ASR). The neural network analyzes the spectrograms to convert the audio signal into text. This deep learning approach allows the system to transcribe speech with high precision, providing the foundation for subsequent processing tasks.  Text-to-Text (TTT): Once the speech is converted into text, it’s processed by powerful natural language models like GPT-4. This stage involves understanding the context, translating languages if needed, and generating appropriate responses. It’s the cognitive core of the system, where raw input text is turned into a meaningful output.  Text-to-Speech (TTS): Finally, the processed text is converted back into spoken words. This involves generating new mel-spectrograms that represent the speech, which are then converted into high-quality audio using advanced vocoder models. Startups, as well as industry giants like Google and Amazon, are at the forefront of this technology, producing voices that are nearly indistinguishable from human speech.  Related:How AI Can Help (Or Deceive) Gamblers Academic Advancements in Speech AI Although speech recognition systems have been around since the 1950s, a significant breakthrough came in 2014 with Baidu’s pioneering research. Led by Andrew Ng, the team introduced deep learning methods to ASR, fundamentally reshaping the design and implementation of these systems.  Related:Exploring the Positive Impacts of AI for Social Equity Building on these advancements, companies like OpenAI have pushed the envelope further. OpenAI’s Whisper, released in September 2022, stands at the forefront of speech AI models. As an open-source model, Whisper has not only set new standards for accuracy and versatility but has also spurred the growth of speech AI companies that leverage its capabilities to develop human-like conversational systems.  Today’s speech-to-text models can closely replicate the intonation, emotion and cadence of human voices, with companies like Eleven Labs — now valued at over $1 billion — leading the charge. The convergence of these advancements has led to the development of sophisticated speech AI systems like OpenAI’s “advanced voice mode.” With its recent rollout to paying users, we are beginning to see the real-world applications of this powerful technology.   Transformative Use Cases Speech-to-speech AI holds immense potential across various applications, including enhancing accessibility for individuals with vision impairments and bridging language gaps in global business, including:  Empowering individuals with vision impairments: Historically, individuals with blindness and vision loss — numbering over 1.1 billion globally — have faced barriers in knowledge-based roles due to reliance on visual data and text-heavy interfaces. Speech-to-speech AI, combined with computer vision technology, is changing how these individuals interact with both physical and digital environments. For example, Be My Eyes uses GPT-4o alongside computer vision to provide real-time audio descriptions of visual surroundings, like iconic landmarks, enhancing the user’s spatial awareness.   Related:China’s DeepSeek Dethrones ChatGPT as US Tech Stocks Plunge Bridging language gaps in global business: On a global scale, with more than 7,000 languages spoken worldwide, speech-to-speech AI is breaking down language barriers that have traditionally hindered international trade and collaboration. Real-time translation capabilities enable seamless communication across different languages, fostering trust and cooperation among global partners. For instance, a business executive in Tokyo can now engage in smooth, multilingual meetings with colleagues in São Paulo, overcoming linguistic obstacles and enhancing global business operations.   The Future of Speech-to-Speech AI  We are on the cusp of a major shift in speech-to-speech technology. Recent advancements are pushing the boundaries by developing unified models that move beyond the traditional three-layer approach, speech-to-text, text-to-text, and text-to-speech. Researchers are exploring direct speech-to-speech systems that bypass text altogether, aiming to reduce latency and enhance the fluidity of translations. These innovations promise to make interactions with AI more seamless and intuitive. In the near term, such developments will significantly improve conversational experiences, while future advancements may address challenges like real-time interruptions and dynamic query changes, with startups already exploring ways to pause and redirect AI processing in more natural and responsive ways.  Moving forward, the key will be to ensure that these innovations are accessible to all and that their benefits are equitably distributed. By doing so, we can harness the power of speech-to-speech AI not just to enhance productivity and economic growth, but to build a more inclusive and connected global community.  source