The commission made that statement Wednesday (July 31) when submitting comments to the Federal Communications Commission (FCC) about its efforts in the AI space.

The FTC is working “to address the rapid emergence of new technologies powered by AI and their potential risks to consumers and businesses,” it said, taking “action against companies that deceive users about their use of AI or use AI in unfair ways.”

The commission gives the example of its allegation that Amazon and Ring used private data — voice recordings collected by Amazon’s Alexa voice assistant and videos collected by Ring’s internet-connected home security cameras — to train their algorithms while violating customers’ privacy.

Ring reached a $5.8 million settlement with the FTC last year. The commission has also been investigating recent AI efforts by Big Tech companies, such as Amazon’s relationship with the AI firm Adept and Microsoft’s hiring of the leadership of Inflection AI.

The FTC also pointed to its efforts to combat AI-powered voice cloning, noting that scammers are using this “technology to impersonate family or friends, business executives or others to obtain money from consumers.”

The FCC in February voted to make it illegal for companies to use AI-generated voices in robocalls, a ruling that gives state attorneys general another tool to use against voice cloning scams: they can they can prosecute fraudsters for not only the scam but also for using AI to generate the voice in the robocall.

“We’re putting the fraudsters behind these robocalls on notice,” FCC Chairwoman Jessica Rosenworcel said in a news release. “State Attorneys General will now have new tools to crack down on these scams and ensure the public is protected from fraud and misinformation.”

Last week, the FTC joined its counterparts from the European Union and the United Kingdom in issuing a rare joint statement about potential antitrust issues in the AI field.

The statement outlined concerns about market concentration and anti-competitive practices in generative AI — the technology fueling popular chatbots like ChatGPT.

“There are risks that firms may attempt to restrict key inputs for the development of AI technologies,” the regulators warned, highlighting the need for quick action in the rapidly evolving sector.

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Integrating AI across various sectors marks a shift in how companies monetize technology and engage with users. From search engines and productivity tools to freelance marketplaces, AI is becoming a key differentiator and revenue driver. This trend is reshaping business models and consumer interactions while also raising questions about the future of work and the implications of AI deployment.

“Companies are increasingly focusing their AI spending on developing lightweight and compressed models, which are crucial for efficient deployment in resource-constrained environments,” Jiahao Sun, CEO at Flock.io, told PYMNTS.

Tech Giants Lead AI Monetization

Google has been integrating AI capabilities into its search engine through initiatives like Search Generative Experience (SGE). Google CEO Sundar Pichai has emphasized, “AI is the most profound technology we are working on today.”

Not to be outdone, Microsoft has launched Copilot, an AI assistant embedded across its Office suite, charging enterprise users $30 per person per month. Microsoft CEO Satya Nadella noted, “Copilot is already improving productivity for more than 40% of the Fortune 100 who participated in our early access program.”

Amazon leverages AI for personalized product recommendations and search results. In a shareholder letter, Amazon CEO Andy Jassy stated, “We’re investing heavily in large language models and generative AI across our businesses.”

“Generative AI will significantly impact how people discover topics on the internet, such as asking ChatGPT for recommendations rather than browsing through search rankings and reviews,” Brad Null, head of AI at Reputation, told PYMNTS. He added, “AI will consume all of the information out there about a business to make such recommendations, so brands will need to stay on top of those advancements, especially when it comes to their search strategy.”

Industry experts are expressing optimism about AI’s potential in the commerce sector while warning of hurdles in its implementation. Their insights paint a picture of an industry on the cusp of major change, grappling with both excitement and caution.

Sun highlighted AI’s capabilities. “Advancements in AI, particularly in large language models [LLMs] and machine learning [ML], are poised to revolutionize the commerce sector by automating a wide range of processes,” he stated. This automation, Sun suggested, could streamline operations across the board, from inventory management to customer service.

AI could enhance customer insights. Null said, “For years, we have had tools that mine customer feedback data to surface insights about brands. With new advancements in AI, these tools are getting increasingly more powerful, helping us more quickly aggregate feedback, discover emerging themes, and surface more actionable insights.” This improved ability to understand and respond to customer needs could give businesses a competitive edge.

However, both experts quickly pointed out that the road to AI integration is fraught with challenges. Sun highlighted the financial barriers, stating, “There are high API fees associated with using centralized AI services, which can quickly escalate as usage scales.”

He added, “Companies must frequently upgrade their hardware, particularly GPUs, to keep up with the latest AI developments and model requirements.” These costs could prove prohibitive for smaller businesses or those operating on tight margins.

Data management remains a critical issue, even as AI capabilities advance. “The biggest challenge today is the same as what it was five years ago, getting the most useful, actionable data and positioning it so that you can maximize value from that data,” Null said. This sentiment underscores the importance of not just having data but also having it in a format that AI can effectively use.

He elaborated on this point, saying, “If you don’t already have the data you need, and have it formatted in a way that it is easy to leverage — meaning, if you haven’t already applied AI and ML to your data — then you probably have a lot of work to do to gather and position this data before using it to find consumer insights.” This suggests that many businesses face a preparatory phase before leveraging AI’s capabilities.

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The release of developer betas for iOS 18.1, iPadOS 18.1, and macOS Sequoia 15.1 introduces many of the Apple Intelligence capabilities previewed at WWDC 2024. This move signals Apple’s intent to transform the mobile commerce landscape, altering how consumers search for products, make purchasing decisions and communicate with brands.

“Apple’s been a bit behind in the AI race,” Bob Rogers, data scientist and CEO of Oii.ai, told PYMNTS. “By enhancing Siri and summaries with AI, I think users will be able to take advantage of ‘conversational’ searching.”

AI Features Debut in Beta

The new betas include writing tools, natural language search capabilities for Photos, priority message sorting for Mail and Siri improvements across iPhone, iPad and Mac platforms. Future updates are expected to include some anticipated features, such as Image Playground and Genemoji.

The advancement in voice search capabilities could significantly streamline the shopping process. Steven Athwal, CEO and founder of The Big Phone Store, told PYMNTS, “Users can speak naturally and conversationally, meaning searches are less focused on hitting keywords. So it’s faster and easier to find what they need, and search is done quicker.”

This improvement addresses current limitations in Siri’s capabilities. Rogers pointed out, “Right now, Siri is a tad lacking, often answering ‘here’s what I found on the web’ when you ask it anything more complex than the current time or weather.”

The potential for more personalized shopping experiences is a key feature of the new AI capabilities. “Siri’s contextual awareness understands follow-up questions and gives accurate results based on previous searches, so a more personalized experience and targeted ads can be implemented,” Athwal said.

AI-generated summaries could also streamline product comparisons. Rogers said, “The AI-generated summaries will help consumers compare products they’re searching for via Siri much easier since it can aggregate product details, reviews, pricing, and more.” Athwal noted this could drive companies to offer more competitive prices to be suggested first by AI’s comparative pricing.

Reshaping Business Communication

These advancements could improve business communication. “AI-driven responses are personal to customers based on their data, preferences, and previous interactions … so you get more advanced and relevant offers,” Athwal said. He also highlighted the potential for “multi-lingual capabilities through AI translation,” allowing businesses to “expand globally much easier.”

Integrating AI in customer service could also lead to more consistent support. Athwal noted, “Support is available 24/7 with a consistent tone and high standard of service, ensuring that real people are not subjected to potential verbal abuse from customers.”

These tools could also provide valuable insights. “By collecting and analyzing customer interactions and speech patterns, businesses can find out what customers are really looking for, what are the trends and needs?” Athwal said.

This separate x. 1 beta confirms the staggered release of Apple Intelligence features, which aligns with recent reports that these capabilities won’t be part of the initial iOS 18, iPadOS 18, and macOS Sequoia releases. Instead, they’re slated for subsequent updates, likely following public betas expected later this summer.

As Apple ventures further into the AI realm, the impact on mobile commerce and customer interactions could be profound. However, concerns about potential e-waste issues have been raised. Athwal warned, “As new features are introduced, the demand for older models will dwindle, and we’ll have an even bigger e-waste problem than we already do.”

Yet, the promise of increased productivity may drive upgrades. “New features can increase productivity, and with smarter organizational tools, workflow, and efficiency will be increased in those who choose to upgrade,” Athwal said.

The rollout of these features not only reshapes the digital marketplace landscape but also raises questions about consumer behavior, device longevity and the pace of technological advancement. Athwal pondered, “An upgrade may seem like a long-term investment but how long before the latest models become outdated? A few years or just a few months?”

As Apple’s AI push unfolds, the mobile commerce landscape is clearly on the brink of significant change, with implications that reach far beyond simple software updates.

The post Apple’s AI Makeover Begins With Developer Beta Release and Siri appeared first on PYMNTS.com.

The tech giant reported that Azure AI, its cloud-based AI platform, grew 30% year-over-year and now accounts for 8 percentage points of Azure’s overall growth. This comes as Microsoft grapples with capacity constraints in meeting the explosive demand for AI services.

“Azure growth included eight points from AI services, where demand remained higher than our available capacity,” said Amy Hood, Microsoft’s chief financial officer. She added that the company expects “Azure growth to accelerate as our capital investments create an increase in available AI capacity to serve more of the growing demand” in the second half of fiscal year 2025.

To meet this demand, Microsoft is ramping up its capital expenditures. Hood revealed that about half of the company’s capital spending is on “land and builds and finance leases” for data centers, investments that will “be monetized over 15 years and beyond.” The other half is dedicated to “CPUs and GPUs to serve customers based on demand signals.”

The company stated it expects capital expenditures to increase in fiscal year 2025 to support growing AI and cloud demand but did not give a precise figure.

Satya Nadella, Microsoft’s CEO, emphasized the company’s focus on capturing the AI opportunity: “We are investing for the long term in our fundamentals, in our innovation, and in our people.” He likened the current AI transition to the earlier shift to cloud computing, describing both as “knowledge and capital-intensive investments.”

Microsoft saw revenue soaring 15% to $64.7 billion and net income jumping 10% to $22 billion. The company’s cloud juggernaut, Azure, led the charge with a 29% revenue surge. Executives touted AI leadership and highlighted record bookings, with Microsoft Cloud revenue hitting $36.8 billion. The Intelligent Cloud segment was the star performer, growing 19% to $28.5 billion. Even gaming got a boost, with Xbox content and services revenue skyrocketing 61%, thanks largely to the Activision acquisition. With these results, Microsoft wrapped up the fiscal year 2024 boasting $245.1 billion in revenue and $88.1 billion in net income, both up over 20% year-over-year.

Copilot Drives Growth Across Products

The impact of AI is being felt across Microsoft’s product lines. Nadella highlighted the success of Copilot for Microsoft 365, the company’s AI-powered assistant for productivity software.

“The number of people who use Copilot daily at work nearly doubled quarter over quarter,” he said, adding that “Copilot customers increased more than 60% quarter over quarter.”

Perhaps most telling was Nadella’s revelation about GitHub Copilot, the company’s AI pair programming tool: “Copilot accounted for over 40% of GitHub revenue growth this year, and is already a larger business than all of GitHub was when we acquired it.”

This success in developer tools appears to be a blueprint for Microsoft’s broader AI strategy. Nadella explained, “What used to be line of business applications to us are Copilot extensions going forward. So we think of this as really a new design system for knowledge and frontline work to drive productivity, which will be very akin to what has happened in software engineering.”

The company sees potential for AI to transform various business functions. “When you think about marketing or finance or sales or customer service, we’ll effectively replicate what you just said, which is the type of productivity we’ve seen in developers will come to all of these functions,” Nadella predicted.

Microsoft’s AI push extends to its Dynamics 365 business applications as well. Nadella pointed to the contact center as an area ripe for AI-driven transformation: “We ourselves are … on a course to save hundreds of millions of dollars in our own customer support and contact center operations. I think we can drive that value to our customers.”

Despite the heavy investments in AI infrastructure, Microsoft remains focused on maintaining financial discipline. Hood stated, “We will remain disciplined on operating expense management,” and projected that operating expenses would grow in the single digits for fiscal year 2025.

The company’s ability to leverage its existing cloud infrastructure for AI workloads appears to be paying dividends. Hood explained, “Because we’re building to one Azure AI stack, we don’t have to have multiple infrastructure investments. … It does, in fact, make margins start off better and obviously scale consistently.”

To address near-term capacity constraints, Microsoft has formed partnerships with other tech companies. “We’ve signed up with third parties to help us, as we are behind with some leases on AI capacity,” Hood said. Nadella added that these partnerships are “no different than leases that we would have done in the past” and may even be “more efficient leases because they’re even shorter dates.”

Future of AI and Microsoft

Looking ahead, Microsoft expects its AI investments to drive growth across its cloud and software businesses. The company is betting that AI will not only enhance existing products but also create new categories of software and services.

As Nadella put it, “At the end of the day, GenAI is just software.” But it’s software that Microsoft believes will reshape the technology landscape and drive the next wave of productivity gains across industries.

With its deep pockets, vast cloud infrastructure, and broad software portfolio, Microsoft appears well-positioned to capitalize on the AI boom. However, the company will need to navigate challenges such as capacity constraints, potential regulatory scrutiny, and competition from other tech giants also investing heavily in AI.

As the race to dominate the AI era heats up, investors and industry observers will closely watch Microsoft’s ability to execute its ambitious AI strategy while maintaining financial discipline.

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The feature, which utilizes the company’s GPT-4o model, offers hyper-realistic audio responses, according to a Tuesday (July 30) TechCrunch report. The new audio capabilities supposedly enable users to have real-time, delay-free conversations with ChatGPT and even interrupt it mid-sentence, addressing key challenges in achieving realistic AI interactions.

The alpha version of Advanced Voice Mode is being released to a select group of ChatGPT Plus subscribers, with plans for a broader rollout to all premium users in this fall. This cautious approach comes after controversy surrounding the technology’s initial demonstration in May.

During that showcase, the voice capability, dubbed “Sky,” drew attention for its uncanny resemblance to actress Scarlett Johansson’s voice, even as the actress said she had repeatedly denied OpenAI permission to use her voice.

Johansson, who had a starring role in the AI-themed film “Her,” subsequently sought legal counsel to protect her likeness. OpenAI denied using Johansson’s voice but removed the controversial demo, highlighting the complex legal landscape surrounding AI and celebrity likeness rights.

To mitigate potential misuse, OpenAI has limited the system to four preset voices created in collaboration with paid voice actors. The company emphasized that ChatGPT cannot impersonate specific individuals or public figures, a measure designed to prevent the creation of deceptive deepfakes — a growing concern in the AI industry.

“We tested GPT-4o’s voice capabilities with 100+ external red teamers across 45 languages,” the company wrote on X, formerly Twitter, in a series of posts on Tuesday to announce the new offering. “To protect people’s privacy, we’ve trained the model to only speak in the four preset voices, and we built systems to block outputs that differ from those voices. We’ve also implemented guardrails to block requests for violent or copyrighted content.”

We tested GPT-4o’s voice capabilities with 100+ external red teamers across 45 languages. To protect people’s privacy, we’ve trained the model to only speak in the four preset voices, and we built systems to block outputs that differ from those voices. We’ve also implemented…

— OpenAI (@OpenAI) July 30, 2024

OpenAI has also implemented filters to block requests for generating music or copyrighted audio, a move likely influenced by recent legal actions against AI companies for alleged copyright infringement.

The music industry, in particular, has been proactive in challenging AI-generated content, with lawsuits already filed against AI song-generators Suno and Udio.

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These moves signal a shifting landscape in AI governance across the tech, business and healthcare sectors.

EU Regulator Probes X’s Covert AI Data Collection

The Irish Data Protection Commission (DPC) has reportedly launched an inquiry into X’s latest privacy rule, which caught European regulators off guard. The social media giant, formerly known as Twitter, quietly introduced a new setting that has stirred the data protection community.

At the heart of the controversy is a default-enabled option allowing X to harvest users’ public posts for Grok, an AI system developed by xAI — another venture by Elon Musk. This move, implemented without fanfare, potentially affects data on millions of EU citizens.

Users face significant hurdles when opting out of this data collection scheme. Currently, the option to disable the setting is only available through X’s web interface, leaving mobile users without recourse. While X has promised a mobile solution in the future, critics argue that this vague timeline is inadequate and potentially violates EU data protection principles.

The DPC, X’s primary EU regulator, expressed dismay at the sudden rollout and stressed the lack of proper consultation. This stealth approach has raised serious questions about user consent and data protection practices.

This latest controversy adds to X’s mounting regulatory challenges in the EU. The company is reportedly already under scrutiny for at least five other investigations related to data protection violations. Each case carries the potential for substantial fines, which could significantly impact X’s financial standing.

As the regulatory storm brews, xAI continues its aggressive expansion. Recently, after securing a staggering $6 billion in funding, the company is constructing what it claims will be a revolutionary AI training supercomputer. This digital powerhouse, boasting 100,000 GPUs, aims to push the boundaries of AI capabilities.

Unilever Rolls Out AI Governance Program

Consumer goods giant Unilever has implemented an AI assurance process, positioning itself ahead of impending European Union regulations on AI use.

At the core of Unilever’s approach is a cross-functional team of experts who scrutinize potential AI projects before they are greenlit. This team, which includes external partners such as Holistic AI, assesses proposals for potential risks and develops mitigation strategies.

Unilever’s Chief Data Officer Andy Hill wrote on the company’s website that the AI assurance process has become integral to Unilever’s operations, with the company recently surpassing 150 ‘projects assured.’ Unilever currently employs over 500 AI systems worldwide, spanning areas from research and development to inventory management and marketing.

“We see potential in the use of AI to drive productivity, creativity, and growth at Unilever,” Hill wrote. He emphasized the importance of responsible implementation as AI deployments expand within the company.

The program’s development comes as the EU prepares to enforce the AI Act, widely regarded as the world’s first comprehensive AI legislation. Unilever’s Chief Privacy Officer Christine Lee noted that regulatory compliance is a key component of the firm’s framework, with the company actively monitoring and addressing upcoming legal developments that may impact its operations.

Unilever’s initiative addresses various AI-related concerns, including intellectual property rights, data privacy, transparency and potential bias. The company reports that its approach is designed to be adaptable, allowing it to keep pace with evolving regulations in different jurisdictions.

As global discussions on AI governance intensify, Unilever executives said they are committed to aligning with legal developments affecting their businesses and brands. This proactive stance, they said, enables the company to pursue digital innovation while maintaining responsible AI use and proper data governance.

Utah Targets AI Mental Health Chatbots

Utah’s newly minted Office of Artificial Intelligence is taking aim at mental health chatbots, marking a first in state-level AI regulation.

The office plans to introduce legislation by year-end to oversee AI use in mental health services, Fierce Healthcare reported. This initiative focuses on AI chatbots employed in licensed medical practice, addressing concerns over reliability and potential legal pitfalls. Key issues include information accuracy and the risk of unlicensed medical practice.

The office is collaborating with a diverse group of stakeholders, from local health providers to national mental health companies and startups, to shape the proposed regulations. Their primary concern is the impact of AI chatbots on patient well-being and the integrity of mental health care delivery.

Utah’s move could spark a domino effect, potentially leading to a patchwork of state regulations and spurring federal action on AI governance in healthcare. As the first state to establish a permanent AI regulatory body, Utah is setting a precedent in navigating the complex intersection of AI technology and mental health policy.

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Boasting 405 billion parameters, the AI model claims performance comparable to proprietary competitors like GPT-4 and Claude 3.5 Sonnet. As Meta expands its reach, with CEO Mark Zuckerberg predicting it will become the most widely used AI assistant by year’s end, businesses are weighing the implications of access to powerful, cost-free AI against the challenges of implementation and security. 

“These models can be used to communicate with customers and provide instant 24/7 assistance with simple queries that do not require human intervention,” Ilia Badeev, head of data science at Trevolution Group, told PYMNTS. “With LLMs [large language models], marketing campaigns and recommendations can be truly personalized for individual customers.” 

Some experts predict a fundamental shift in customer service. “If you think about the cost of intelligence effectively going to zero over time for customer relations, call centers will not exist in the future. AI systems will manage huge volumes of customer inbound in a meaningful and satisfactory way to the end user,” Mike Conover, CEO of the AI company Brightwave, told PYMNTS.

The potential for businesses to customize these models is significant. “By fine-tuning Llama on their specific domain data, companies can create powerful natural language interfaces that understand customer queries, provide intelligent recommendations, and automate tasks like product categorization and content generation,” Hamza Tahir, CTO and co-founder of ZenML, an open-source machine learning operations (MLOps) startup, told PYMNTS.

Opportunity for Small Businesses?

The availability of powerful, open-source AI models could level the playing field for smaller businesses. “Open-source models like Llama have the potential to democratize AI-powered commerce tools for small businesses and startups,” Tahir said.

“Even small teams can leverage state-of-the-art natural language processing capabilities to build intelligent chatbots, product recommenders and content generators.” 

Open-source AI also offers advantages in regulatory compliance. “Processing data with in-house models keeps user data private and compliant with regulatory laws (such as GDPR),” Badeev pointed out, referring to the EU’s General Data Protection Regulation. This contrasts with proprietary models that may require sending user data to third-party services.

The introduction of Llama 3.1 is stirring debate about its potential to disrupt the commercial AI market. Conover said that the 405 billion-parameter model from Llama is comparable in its reasoning quality to OpenAI’s GPT-4. “This means commercial providers do not have some secret sauce that would lead to vendor lock-in — business owners are the masters of their own destiny,” he added. 

Tahir predicted that the introduction of the new model may presage a shift toward a service-based model, where AI companies differentiate themselves through their domain expertise, data assets and ability to customize and deploy open-source models for specific use cases.

The economic impact could be substantial. “For business owners like eCommerce platforms and customer service providers, you’re going to see improving unit economics for these services because of the competitive pressures that open-source technologies place on the commercial providers,” Conover added.

Despite the opportunities, businesses face challenges in implementing open-source AI. “Open-source AI models give SMEs [small to mid-sized enterprises] the advantage of doing more and reaching a wider audience, but this comes at the cost of both talent and security, Harry Toor, chief of staff at OpenSSF, which promotes open-source software, told PYMNTS. 

He added, “Open-source AI models need to be consumed securely to ensure outputs aren’t manipulated, which could sink any SME operating in a cost-constrained environment.” 

Security measures are crucial. “Secure open-source AI models should be built from a secured development environment, cryptographically signed, and follow best practices already in place for open-source software development. This can be achieved by leveraging existing open-source tools from OpenSSF and elsewhere to secure open-source AI models,” Toor said.

Future of AI in Commerce

Potential supply chain issues also pose a risk. “The commercial AI market needs to evaluate the supply chain for open-source AI models. Recent global cyber issues like XZ Utils and the Microsoft Blue Screen of Death have shown that widely used software components can cripple industries,” Toor warned.

As businesses consider adopting open-source AI, they face a complex set of considerations. The long-term implications for eCommerce, customer service and marketing strategies are still unfolding. While some predict a radical transformation of these sectors, others caution that the technology’s impact will depend on factors beyond mere availability.

Open-source models could lead to more effective feedback collection. “User feedback/reactions can be effectively gathered from various sources such as reviews, social media mentions, and customer support interactions. These can be massively processed using AI to extract insights and analytics instantly,” Badeev noted.

For all PYMNTS AI coverage, subscribe to the daily AI Newsletter.

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According to OpenAI board member Adam D’Angelo, that milestone is likely to happen “within five to 15 years,” Seeking Alpha reported Monday (July 29).

D’Angelo, CEO and co-founder of Quora, made that prediction during an event last week, the report added. He said the advent of artificial general intelligence (AGI) will be a “very, very important change in the world when we get there.”

His comments follow reports from earlier this month that OpenAI had developed a way to track its progress toward building AGI, with the company sharing a new five-level classification system with employees.

The company believes it is now at Level 1, where AI that can interact in a conversational way with people, and is approaching Level 2, or systems that can solve problems as well as a human with a doctorate-level education.

The next levels involve AI systems that can spend several days acting on a user’s behalf, develop innovations, and finally — at level five — do the work of an organization.

OpenAI CEO Sam Altman and Chief Technology Officer (CTO) Mira Murati said last fall that AGI will be reached within the next 10 years.

“We’re big believers that you give people better tools, and they do things that astonish you,” Altman said. “And I think AGI will be the best tool humanity has yet created.”

As PYMNTS wrote recently, the reports of these efforts have sparked buzz in the business world of the possibility of AI-powered commerce that could rewrite the rules of global trade, assuming the technology can live up to the hype.

“OpenAI’s pursuit of human-level reasoning isn’t just a technological marvel; it’s a narrative of pushing boundaries and sparking new possibilities in every sector,” Ghazenfer Mansoor, founder and CEO of Technology Rivers, told PYMNTS. “In business, AI can dramatically change how supply chains are managed, forecast market trends with great accuracy, and make customer experiences very personal on a big scale.”

Earlier this year, OpenAI staffers reportedly showed demos of AI models that could answer tricky science and math questions, with one model scoring more than 90% on a championship math dataset. The company also recently showcased a project with new human-like reasoning skills at an internal meeting.

“The way such an algorithm can work is by creating multiple options, following a tree of possibilities, and then reasoning about the outcome and choosing the best path,” SmythOS CTO Alexander De Ridder told PYMNTS. “This is similar to how chess players think different steps ahead before choosing to move their piece.”

He suggested that OpenAI’s innovation likely involves “an algorithmic breakthrough in how to do this efficiently and scalably,” potentially combining “autonomous web research and tool usage to arrive at a reasoning breakthrough.”

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The release of Meta’s Llama 3 family of models, mostly available for free, marks a significant escalation in the AI arms race and could redefine how businesses operate across sectors.

Meta’s flagship Llama 3 model, boasting 405 billion parameters, demonstrates multilingual prowess in eight languages and improved capabilities in code generation and problem-solving. This launch follows closely on the heels of OpenAI’s GPT-4o mini and Google’s upgraded Gemini.

The Llama 3 family includes three models of varying sizes: 8 billion, 70 billion and 405 billion parameters. All three models feature expanded “context windows,” allowing them to process larger amounts of information and handle more complex, multi-step requests.

A New Era of AI Capabilities

OpenAI has recently added to its lineup the GPT-4o mini, a smaller version of the GPT-4o model that was introduced in May.

“We expect GPT-4o mini will significantly expand the range of applications built with AI by making intelligence much more affordable,” OpenAI said in a release announcing its launch.

This model maintains much of the functionality of its larger counterpart, including a context window of 128,000 tokens, which is eight times that of GPT-3.5 Turbo.

Google’s Gemini, released in three versions — Ultra, Pro and Nano — is designed to be more efficient and perform better across various tasks. Gemini Ultra has shown strong performance in complex reasoning and multimodal tasks, rivaling human experts in certain benchmarks.

These models are pushing the boundaries of what’s possible in artificial intelligence, with each company claiming superior performance in various benchmarks.

OpenAI reports that GPT-4o mini outperforms competitors on several standard tests, including the massive multitask language understanding (MMLU) benchmark, where it scored 82% compared to Google’s Gemini Flash at 77.9% and Anthropic’s Claude Haiku at 73.8%.

Google has also unveiled significant upgrades to its Gemini AI platform, enhancing its capabilities and expanding its reach. The company is rolling out Gemini 1.5 Flash, a faster and more capable version, to users of the free tier in over 230 countries and territories. This update quadruples the context window to 32,000 tokens, allowing for longer conversations and more complex queries. Additionally, Google is introducing a new feature to combat AI hallucinations by displaying related content links within Gemini’s responses.

The tech giant is also broadening Gemini’s accessibility. The Gemini mobile app is being introduced to more countries, while integration with Google Messages is expanding to the European Economic Area, U.K. and Switzerland. In a move to engage younger users, Google plans to extend Gemini access to teenagers globally in over 40 languages, implementing additional safeguards and partnering with child safety experts.

Transforming Business Across Sectors

The implications for commerce are far-reaching.

OpenAI suggests that GPT-4o mini’s larger context window and improved capabilities make it “especially useful for processing big documents or linking multiple interactions with the AI model.”

This could lead to enhanced recommendations in online stores, faster real-time text responses for customer service and more accurate and detailed answers for students.

From Main Street to Wall Street, businesses are eyeing these AI advancements as potential game-changers. With AI-powered chatbots offering more nuanced, round-the-clock support, customer service will likely see an immediate impact. eCommerce giants are poised to leverage these models for hyper-personalized product recommendations and dynamic pricing strategies.

Supply chain management, a persistent pain point for many industries, could see a significant overhaul. AI models promise to optimize inventory levels and distribution networks with unprecedented accuracy, potentially slashing operational costs and boosting responsiveness to market fluctuations.

In the financial sector, risk assessment and fraud detection are prime targets for AI enhancement. Robo-advisors powered by these advanced models could democratize access to sophisticated financial planning, disrupting traditional wealth management services.

Healthcare isn’t far behind, with AI poised to accelerate drug discovery and enhance diagnostic accuracy. These models’ ability to analyze vast amounts of medical data could lead to breakthroughs in personalized medicine and treatment protocols.

Challenges and Opportunities Ahead

For marketing departments, AI-generated content could be a double-edged sword. While it promises to streamline content production, concerns about AI-generated misinformation loom large, challenging brands to maintain authenticity and trust.

Meta’s strategy of offering Llama 3 largely for free could democratize access to cutting-edge AI capabilities, potentially leveling the playing field for startups and smaller enterprises. Similarly, OpenAI’s introduction of GPT-4o mini at “just over half the price per token of GPT-3.5 Turbo” aims to make AI more accessible to a broader range of businesses.

As these AI models evolve, their impact on commerce is expected to accelerate. Future iterations, including multimodal versions incorporating image, video and speech capabilities, could spark a new wave of innovation across industries. OpenAI has already hinted at expanding GPT-4o mini’s capabilities to include “image, video and audio inputs and outputs.”

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This rapidly evolving field tackles a crucial question: How do we ensure that intelligent machines serve humanity’s best interests? From privacy concerns to racial bias, from job displacement to existential risks, AI ethics grapples with the moral implications of our increasingly automated world.

AI ethics encompasses a wide range of concerns, including privacy, bias, transparency, accountability, and the long-term societal impacts of artificial intelligence. As AI systems become more sophisticated and autonomous, the ethical questions surrounding their development and deployment grow increasingly complex and urgent.

The Bias Blind Spot: When AI Amplifies Inequality

One of the primary areas of focus in AI ethics is algorithmic bias. AI systems are only as unbiased as the data they’re trained on and the humans who design them. The consequences can be far-reaching and profound when these systems reflect or amplify existing societal biases.

A stark example of this issue emerged in 2018 when Amazon scrapped an AI recruiting tool that showed bias against women. The system, which was designed to review resumes and identify top talent, had been trained on patterns in resumes submitted to the company over a 10-year period. Because tech industry applicants were predominantly male then, the AI learned to penalize resumes that included the word “women’s” or mentioned all-women’s colleges.

This case highlighted the potential for AI to perpetuate and even exacerbate existing inequalities if not carefully designed and monitored. It also underscored the need for diverse teams in AI development to help identify and mitigate such biases.

The problem of algorithmic bias extends far beyond hiring practices. A study published in Science found that a widely used algorithm in U.S. hospitals was systematically discriminating against black patients. The algorithm, used to identify patients who would benefit from extra medical care, was unintentionally programmed to use health costs as a proxy for health needs. Because less money has historically been spent on black patients due to socioeconomic factors and disparities in access to care, the algorithm incorrectly concluded that black patients were healthier than equally sick white patients.

Another critical concern in AI ethics is privacy. As AI systems become more adept at collecting, analyzing and utilizing personal data, questions arise about the appropriate limits of such capabilities. The use of facial recognition technology by law enforcement agencies has sparked particular controversy, with critics arguing that it represents an unacceptable intrusion into personal privacy and civil liberties.

The privacy implications of AI extend beyond facial recognition. In 2019, it was revealed that Google’s AI assistant could eavesdrop on private conversations through its Nest security system. The incident highlighted the potential for AI-powered smart home devices to become surveillance tools, raising questions about the balance between convenience and privacy in an increasingly connected world.

Transparency and explainability represent another key pillar of AI ethics. As AI systems become more complex and make decisions that significantly impact people’s lives — from loan approvals to medical diagnoses — there’s a growing demand for them to explain their reasoning in terms that humans can understand.

This issue arose in the healthcare sector when IBM’s Watson for Oncology, an AI system designed to assist in cancer treatment recommendations, faced criticism for its lack of transparency. Oncologists expressed concern that they couldn’t understand how the system arrived at its recommendations, making it difficult to trust and implement its advice in critical care situations.

The Trolley Problem 2.0: Ethics in Autonomous Systems

As AI systems become more autonomous, questions of accountability also come to the forefront. When an AI makes a decision that results in harm, who bears responsibility — the developers, the company deploying the system or the AI itself?

This question has practical implications in areas like autonomous vehicles. In 2018, an Uber self-driving car struck and killed a pedestrian in Arizona, raising questions about liability and the ethical considerations in programming such vehicles. Should an autonomous car prioritize the safety of its passengers or pedestrians in unavoidable accident scenarios? These “trolley problem” type dilemmas have moved from philosophical thought experiments to real-world engineering challenges.

Similar ethical dilemmas arise in the field of autonomous weapons systems. The prospect of “killer robots” capable of selecting and engaging targets without human intervention has sparked intense debate. While proponents argue that such systems could reduce military casualties and potentially be more precise than human soldiers, critics warn of the moral hazard of delegating life-or-death decisions to machines.

Google faced significant employee backlash over its involvement in Project Maven, a U.S. Department of Defense initiative using AI for drone footage analysis. The controversy led to Google deciding not to renew the contract and establishing AI principles that preclude the development of AI for weapons.

Looking ahead, the field of AI ethics must also grapple with long-term existential questions. As AI capabilities continue to advance rapidly, some experts warn that artificial general intelligence (AGI) or artificial superintelligence (ASI) could pose existential risks to humanity if not developed with robust ethical safeguards.

Organizations like the Future of Humanity Institute at Oxford University and the Center for Human-Compatible AI at UC Berkeley are dedicated to researching ways to ensure that advanced AI systems remain aligned with human values and interests. These efforts involve complex technical challenges, such as developing reliable methods to specify and encode human values in AI systems, as well as philosophical questions about the nature of intelligence, consciousness and morality.

In response to these myriad challenges, governments and organizations worldwide are working to establish ethical guidelines and regulatory frameworks for AI development and deployment. The European Union’s AI Act, which aims to create the world’s first comprehensive AI regulations, represents a significant step in this direction.

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