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AI regulation is progressing across the world as policymakers try to protect against the risks it poses without curtailing AI's potential.

In July, Chinese regulators introduced rules to oversee generative AI services. Their focus stems from a concern over the potential for generative AI to create content that conflicts with Beijings viewpoints.

The success of ChatGPT and similarly sophisticated AI bots have sparked announcements from Chinese technology firms to join the fray. These include Alibaba, which has launched an AI image generator to trial among its business customers.

The new regulation requires generative AI services in China to have a licence, conduct security assessments, and adhere to socialist values. If "illegal" content is generated, the relevant service provider must stop this, improve its algorithms, and report the offending material to the authorities.

The new rules relate only to generative AI services for the public, not to systems developed for research purposes or niche applications, striking a balance between keeping close tabs on AI while also making China a leader in this field.

The use of AI in film and TV is one of the issues behind the ongoing strike by Hollywood actors and writers that has led to production stoppages worldwide. As their unions renegotiate contracts, workers in the entertainment sector have come out to protest against their work being used to train AI systems that could ultimately replace them.

The AI proposal put forward by the Alliance of Motion Picture and Television Producers reportedly stated that background performers would receive one day's pay for getting their image scanned digitally. This scan would then be available for use by the studios from then on.

China is not alone in creating a framework for AI. A new law in the US regulates the influence of AI on recruitment as more of the hiring process is handed over to algorithms.

From browsing CVs and scoring interviews to scraping social media for personality profiles, recruiters are increasingly using the capabilities of AI to speed up and improve hiring. To protect workers against a potential AI bias, New York City's local government is mandating greater transparency about the use of AI and annual audits for potential bias in recruitment and promotion decisions.

A group of AI experts, including Meta, Google, and Samsung, has created a new framework for developing AI products safely. It consists of a checklist with 84 questions for developers to consider before starting an AI project. The World Ethical Data Foundation is also asking the public to submit their own questions ahead of its next conference. Since its launch, the framework has gained support from hundreds of signatories in the AI community.

In response to the uncertainties surrounding generative AI and the need for robust AI governance frameworks to ensure responsible and beneficial outcomes for all, the Forums Centre for the Fourth Industrial Revolution (C4IR) has launched the AI Governance Alliance.

The Alliance will unite industry leaders, governments, academic institutions, and civil society organizations to champion responsible global design and release of transparent and inclusive AI systems.

Meanwhile, generative AI is gaining a growing user base, sparked by the launch of ChatGPT last November. A survey by Deloitte found that more than a quarter of UK adults have used generative AI tools like chatbots. This is even higher than the adoption rate of voice-assisted speakers like Amazon's Alexa. Around one in 10 people also use AI at work.

Nearly a third of college students have admitted to using ChatGPT for written assignments such as college essays and high-school art projects. Companies providing AI-detecting tools have been run off their feet as teachers seek help identifying AI-driven cheating. With only one full academic semester since the launch of ChatGPT, AI detection companies are predicting even greater disruption and challenges as schools need to take comprehensive action.

30% of college students use ChatGPT for assignments, to varying degrees.

Image: Intelligent.com

Another area where AI could ring in fundamental changes is journalism. The New York Times, the Washington Post, and News Corp are among publishers talking to Google about using artificial intelligence tools to assist journalists in writing news articles. The tools could help with options for headlines and writing styles but are not intended to replace journalists. News about the talks comes after the Associated Press announced a partnership with OpenAI for the same purpose. However, some news outlets have been hesitant to adopt AI due to concerns about incorrect information and differentiating between human and AI-generated content.

Developers of robots and autonomous machines could learn lessons from honeybees when it comes to making fast and accurate decisions, according to scientists at the University of Sheffield. Bees trained to recognize different coloured flowers took only 0.6 seconds on average to decide to land on a flower they were confident would have food and vice versa. They also made more accurate decisions than humans, despite their small brains. The scientists have now built these findings into a computer model.

Generative AI is set to impact a vast range of areas. For the global economy, it could add trillions of dollars in value, according to a new report by McKinsey & Company. It also found that the use of generative AI could lead to labour productivity growth of 0.1-0.6% annually through 2040.

At the same time, generative AI could lead to an increase in cyberattacks on small and medium-sized businesses, which are particularly exposed to this risk. AI makes new, highly sophisticated tools available to cybercriminals. However, it can be used to create better security tools to detect attacks and deploy automatic responses, according to Microsoft.

Because AI systems are designed and trained by humans, they can generate biased results due to the design choices made by developers. AI may therefore be prone to perpetuating inequalities, and this can be overcome by training AI systems to recognize and overcome their own bias.

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From Hollywood to Sheffield, these are the AI stories to read this month - World Economic Forum

Sanford firmly believes that AI is not just a buzzword but a game changer that holds the key to unlocking extraordinary opportunities within the real estate sphere.

AI is not about replacing real estate professionals; its about enhancing their abilities and the overall customer journey, asserts Sanford, emphasizing his commitment to leveraging AI as a collaborative tool rather than a divisive force in the industry. Unlike those hesitant to embrace change, Sanford recognizes the immense potential AI brings to the table and views it as an indispensable asset that can elevate agents proficiency and effectiveness.

I am an entrepreneur at heart, which means I think like a true entrepreneur, its less about P&L. Im not building a business to fund a lifestyle. Most entrepreneurs would rather be broke than have a mediocre business thats technically profitable, he says. Its this mindset, what he calls the mindset of a person that builds a start up that encourages him to radical things [such as investing in AI], he says. You realize that you can crash and burn a number of times while building something that finally gets traction.

However, Sanford has no plans to crash and burn with the AI-driven solutions tailored explicitly to cater to the ever-changing demands of the modern real estate market. Were starting to make investments into various companies on the edges. We want to create opportunities for people to merge their new ideas inside the city of eXp that would benefit agents, brokers and staff. That includes eXp Ventures, to foster innovation. How do we take from companies that have done well and innovate in a modern way?

By harnessing the power of machine-learning algorithms, eXp Realtys agents can now gain unprecedented insights into market trends, accurately predict property values, and efficiently match buyers with their dream homes.

Weve got a number of instances around the company, and were going to use other instances of either generative AI or image AI. We are already doing some image AI, says Sanford. Were already working AI into our search solutions, like Zoocasa and others. So, youll be able to use natural language search when searching for property. So, the stuff that Zillows doing, were incorporating, he says.

Real estate agents are going to get seriously disrupted by AI, says Sanford, but not in the value of the real estate agent, but more in the way things are done. Think about the [possibility] that lead follow up and nurturing campaigns will be managed by AI in the future. Look at platforms like Synthesia, [an AI video generator]. At eXp, we have a partnership with Blended Sense, [a content creation platform], so agents can do a video using Blended Sense [then upload] that into Synthesia, says Sanford.

The agent can then add in content about their local community thats generated by ChatGPT-4 and pump it into Synthesia. They can self-narrate with their voice using an AI-generated version of themselves with AI-generated content. And in some cases, the consumer wont even know it wasnt the agent actually providing that information, he says.

Sanford envisions a future where AI-driven chatbots effortlessly handle routine inquiries, freeing up valuable time for agents to focus on building deeper connections with clients and offering tailored guidance throughout the real estate journey. The true essence of real estate lies in nurturing meaningful relationships, Sanford says, and AI should serve as a seamless enabler rather than an intrusive barrier in achieving that.

While some may view AI as an accessory, Sanford passionately believes that integrating AI is essential in fortifying the industrys foundation for generations to come. He envisions a day when AI algorithms will go beyond predictive analytics and assist agents in curating personalized property recommendations that align perfectly with their clients preferences and lifestyles.

Moreover, Sanford is not one to rest on his laurels; he relentlessly invests in research and development to push the boundaries of what AI can accomplish for the real estate world. Sanfords commitment to staying ahead of the technological curve is driven by his belief that embracing AI wholeheartedly is not an option but a necessity to remain relevant in an ever-accelerating digital era.

When it comes to integrating AI into your brokerage, Sanford sums it up this way: The reality is that it doesnt matter what the controversy is. Its literally those who dont use AI will work for people who use AI.

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eXp's Glenn Sanford on AI's transformative impact in real estate - HousingWire

The early innings of the artificial intelligence trade may be over, but Citigroup is staying positive on the tech subsector, viewing cash flows as the key to unlocking the winners of the next phase. "In sum, our message is not to be overly deterred by the significant year-to-date move in profitable AI stocks," the bank said in a Friday note to clients. "Medium- to long-term opportunities still exist as the AI theme has an accelerating growth trajectory and attractive [free cash flow] dynamics that should further improve from here." So far this year, anything connected to AI has seen a significant uptick in valuation, with Nvidia shares leading the pack, surging more than 200%. While the jaw-dropping price action may suggest AI is no longer an early trade, Citi reiterated that the "initial positive thesis" looks intact and warned investors to avoid overlooking free cash flows. Citi expects many names to meet accelerated growth expectations and views free cash flows as "increasingly compelling." "Profitable stocks within this theme are already impressive cash generating machines," the bank wrote. "Recent AI developments should accentuate this characteristic and push FCF margins and growth to new highs." Given this setup, Citi screened for AI-related stocks expected to outpace market growth expectations and experience an uptick in free cash flow margins. Here are some of the stocks that made the cut: Amazon has the highest consensus expectation of more than 48% growth over the long term. Shares have gained almost 54% this year as Wall Street rotates back into technology stocks following the slump in 2022. Some investors have viewed the e-commerce giant as lagging behind its peers in the AI race. During an i nterview with CNBC this month, CEO Andy Jassy soothed some of those concerns, reiterating Amazon's plan to invest in AI across segments. Earlier this year , Amazon also unveiled a generative AI service called Bedrock for its Amazon Web Services unit, allowing clients to use language models to create their own chatbots and image-generation services. Competing chatbot heavyweight Alphabet also made the cut. Shares of the Google parent and Bard creator have rallied 38% as it battles it out with Microsoft -backed OpenAI's ChatGPT. Consensus estimates peg long-term growth at more than 17%, with a near-term free cash flow margin of nearly 24%. GOOGL YTD mountain Alphabet shares in 2023 A handful of financial stocks were also included in Citi's screen. Mastercard offers the greatest near-term free cash flow yield of the group, at 48.4%. Its long-term consensus growth estimate hovers around 19%. Shares have gained about 15% year to date. Ford Motor , Match Group and ServiceNow also made the list. CNBC's Michael Bloom contributed reporting.

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Citi stays positive on A.I. theme and lays out the key to finding ... - CNBC

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Antony Blinken & Gina Raimondo: To shape the future of AI, we must ... - Financial Times

The moment that generative AI applications hit the market, it changed the pace of business not only for security teams, but for cybercriminals too. Today, not embracing AI innovations can mean falling behind your competitors and putting your cyber defense at a disadvantage against cyberattacks powered by AI. But when discussing how AI will or won't impact cybercrime, it's important that we look at things through a pragmatic and sober lens not feeding into hype that reads more like science fiction.

Today's AI advancements and maturity signal a significant leap forward for enterprise security. Cybercriminals can't easily match the size and scale of enterprises' resources, skills, and motivation, making it harder for them to keep up with the current speed of AI innovation. Private venture investment in AI exploded to $93.5 billion in 2021 the bad guys don't have that level of capital. They also don't have the manpower, computing power, and innovations that affords commercial companies or government more time and opportunity to fail quick, learn fast, and get it right first.

Make no mistake, though: Cybercrime will catch up. This is not the first time the security industry has had a brief edge when ransomware started driving more defenders to adopt endpoint detection and response technologies, attackers needed some time to figure out how to circumvent and evade those detections. That interim "grace period" gave businesses time to better shield themselves. The same applies now: Businesses need to maximize on their lead in the AI race, advancing their threat detection and response capabilities and leveraging the speed and precision that current AI innovations afford them.

So how is AI changing cybercrime? Well, it won't change it substantially anytime soon, but it will scale it in certain instances. Let's take at a look at where malicious use of AI will and won't make the most immediate impact.

In recent months, we've seen claims regarding various malicious use cases of AI, but just because a scenario is possible does not make it probable. Take fully automated malware campaigns, for example logic says that it is possible to leverage AI to achieve that outcome, but given that leading tech companies have yet to pioneer fully automated software development cycles, it's unlikely that financially constrained cybercrime groups will achieve this sooner. Even partial automation can enable the scaling of cybercrime, however, a tactic we've already seen used in Bazar campaigns. This is not an innovation, but a tried-and-true technique that defenders are already taking on.

Another use case to consider is AI-engineered phishing attacks. Not only is this one possible, but we're already beginning to see these attacks in the wild. This next generation of phishing may achieve higher levels of persuasiveness and click-rate, but a human-engineered phish and AI-engineered phish still drive toward the same goal. In other words, an AI-engineered phish is still a phish searching for a click, and it requires the same detection and response readiness.

However, while the problem remains the same, the scale is vastly different. AI acts as a force multiplier to scale phishing campaigns, so, if an enterprise is seeing a spike in inbound phishing emails and those malicious emails are significantly more persuasive then it's likely looking at a high click-rate probability and potential for compromise. AI models can also increase targeting efficacy, helping attackers determine who is the most susceptible target for a specific phish within an organization and ultimately reaching a higher ROI from their campaigns. Phishing attacks have historically been among the most successful tactics that attackers have used to infiltrate enterprises. The scaling of this type of attack emphasizes the critical role that EDR, MDR, XDR, and IAM technologies play in detecting anomalous behavior before it achieves impact.

AI poisoning attacks, in other words programmatically manipulating the code and data on which AI models are built, may be the "holy grail" of attacks for cybercriminals. The impact of a successful poisoning attack could range anywhere from misinformation attempts to Die Hard 4.0. Why? Because by poisoning the model, an attacker can make it behave or function in whatever way they want, and it's not easily detectable. However, these attacks aren't easy to carry out they require gaining access to the data the AI model is training on at the time of training, which is no small feat. As more models become open source, the risk of these attacks will increase, but it will remain low for the time being.

While it's important to separate hype from reality, it's also important to ensure we're asking the right questions about AI's impact on the threat landscape. There are lots of unknowns regarding AI's potential how it may change adversaries' goals and objectives is one we mustn't overlook. It remains unknown how new abilities may help serve new purposes for adversaries and recalibrate their motives.

We may not see an immediate spike in novel AI-enabled attacks, but the scaling of cybercrime thanks to AI will have a substantial impact on organizations that aren't prepared. Speed and scale are intrinsic characteristics of AI, and just as defenders are seeking to benefit from them, so are attackers. Security teams are already understaffed and overwhelmed seeing a spike in malicious traffic or incident response engagements is a substantial weight added onto their workload.

This reaffirms more than ever the need for enterprises to invest in their defenses, using AI to drive speed and precision in their threat detection and response capabilities. Enterprises that take advantage of this "grace period" will find themselves much more prepared and resilient for the day attackers actually do catch up in the AI cyber race.

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Are AI-Engineered Threats FUD or Reality? - Dark Reading