A price measure that strips out energy costs and the effect of interest-rate changes rose 8.2% from a year earlier, according to data published by Statistics Sweden on Wednesday. That was higher than the median estimate of 7.8% in a Bloomberg survey as well as the 8.1% expected by the Riksbank. 

Some of the increase could be explained by the world premiere of Beyoncés current world tour, according to Danske Bank economists. The US R&B superstar drew more than 80,000 over two nights to Stockholms Friends Arena, and visitors likely contributed to an unexpectedly large increase in hotel and recreation prices, the lenders chief economist Michael Grahn said. 

We expect this upside surprise to be reversed in June as prices on hotels and tickets reverse back to normal, Grahn said. Danske still expects the Riksbank to raise rates further as the Swedish currency remains weak and inflation hasnt slowed as much as the central bank would have liked to see.

Glenn Nielsen, an economist at Swedbank, said that while the gigs may have contributed to push accommodation costs higher in May, the surprisingly strong price increases were fairly broad-based, and that strong demand in general, as well as high cost pressures, give hotels both the ability and the motivation to raise prices.

The reading comes as price pressures are subsiding globally, with data published Tuesday showing that US inflation slowed to its lowest level since March 2021, while consumer prices in the euro zone rose less than expected in May. 

However, Swedish prices are still rising far faster than the central banks target, and the weakness of the Swedish currency complicates the situation for the Riksbank as its efforts are hampered by higher costs of imported goods, mirroring developments in neighboring Norway.

The krona has recently been trading near all-time lows against the euro. That development makes it more urgent for the Riksbank to keep its benchmark rate at a higher level than the European Central Bank, which is widely expected to raise its deposit rate to 3.5% on Thursday. It would bring the ECB rate on par with that of the Riksbank, which has said it expects to hike again later this month or in September.

Most economists have penciled in that the Riksbank will announce a quarter-point rate hike on June 29, and the latest price data seemed to confirm that view, regardless of any temporary Beyonce blip.

All in all, May inflation numbers were a disappointment and came out clearly higher than we had expected, Nordeas Torbjorn Isaksson said. It is a high outcome, not least considering the broad upturn, reinforcing the view that the Riksbank will hike rates in June. 

The country will lose 13,500 individuals with investable wealth of over $1 million this year, projects migration consulting firm Henley & Partners in its Private Wealth Migration Report released earlier this week. China will lose more than twice as many millionaires as India, which sits in second place in Henley and Partners ranking. Around 10,800 millionaires left China in 2022, and the country has led the world in net outflows of high net-worth individuals for the past decade. 

China had around 780,000 millionaires at the end of 2022, the firm estimates, making the country the third wealthiest in the world, behind the U.S. and Japan. Yet the growth in the wealthy population has been negligible since 2017, writes Andrew Amolis, head of research at New World Wealth, which worked with Henley & Partners on the report.

In total, the consulting firm projects that 122,000 millionaires will relocate this year, up from 84,000 last year. The U.K. and Russia take third and fourth place respectively in terms of projected net outflows of millionaires. 

Why are millionaires leaving China?

Chinas wealthy may be trying to escape regulatory scrutiny by relocating.

In mid-2021, president Xi Jinping announced a campaign to support common prosperity, targeting the countrys high level of income inequality. The announcement came alongside an extended regulatory crackdown against some of the countrys largest private companies. 

Calls to spread the wealth have continued even as China now tries to bolster its flagging post-COVID economy. In March, Xi called upon private firms and entrepreneurs to donate to charity and share the fruits of growth.

Chinese millionaires were also frustrated with the countrys tough approach to the COVID pandemic, where snap lockdowns and other disruptive measures were imposed to drive cases down to zero.

Ironically, Beijings decision to roll back its COVID controls controls may now be driving the recent outflow. With the end of international quarantine in early January, Chinese millionaires can now travel overseas to check out possible relocation desinations in person.

Where are Chinese millionaires going?

Australia and the United Arab Emirates top the Henley & Partners list of countries expected to attract the most millionaires this year.

Many wealthy Chinese are also looking at Singapore, which sits in third place in the consulting firms forecast. 

Chinese elite are attracted to Singapores well-developed wealth management sector, its close proximity to China, and its connections to Chinese culture.

Singapores neutral position between the U.S. and China is also a benefit. Chinese entrepreneurs and businesses have based themselves in the country to avoid American scrutiny over business with China.

Incoming Chinese money is good for the local financial industry. The number of family offices in the city jumped from 400 to 700 in 2021, and those in the industry say interest grew more last year, reports Reuters.

But the attention is also driving up the cost of living. The median private home price in Singapore is now $1.2 million, according to the Urban Land Institute, surpassing Hong Kong. The city also has the highest median monthly rents in the region, coming in at almost $2,600. 

A recent study found that only 11% of firms using A.I. have gained financial benefits. Even technology giants have struggled. IBMs $20 billion diagnostic A.I. system Watson Health diagnosed cancer more accurately than doctors in laboratory experiments but flopped in the field. It became a commercial and reputational disaster for the storied American company.

The failure could hardly be blamed on a lack of technical expertise. IBM had tasked an army of engineers to work on Watson. Our extensive research on the challenges of developing A.I. in various commercial settings points to a surprising cause: Watson was developed and brought to market in a way that works well for traditional I.T.but not A.I.. This is due to a fundamental difference between conventional software and A.I.: While the former processes data, A.I. continually learns from the data and becomes better over time, transcending even its intended capabilities if nurtured properly.

Practices analogous to the best parenting styles can accelerate A.I. development. We prescribe an A.I. development approach that is based on nurturing and learning and has been implemented in more than 200 A.I. projects for industrial and other customers.

Let it learn from mistakes

Children do not learn to cycle by watching an educational video but by clambering onto a bike and stepping on the pedals, learning valuable lessons from each painful fallbefore long, the magic happens.

The same logic applies to A.I. Many companies like IBM think they should collect vast amounts of data to perfect the algorithms before deployment. This is misguided. Putting A.I. to work in the real world, rather than sequestering it in controlled environments, helps generate more data that in turn feeds back into the development process.

Although early deployment is inherently riskier, it also initiates a continuous feedback loop through which the algorithm is enriched by new data. Further, it is important that the data stems from both standard and difficult or atypical situations that, taken together, support comprehensive A.I. development.

ChatGPT is a great example. The chatbot was released to the public by OpenAI last November while still wet behind the ears, albeit more for reasons concerned with getting ahead of the competition. In any case, the gamble worked: Not only has ChatGPT become a worldwide phenomenon, leaving the likes of Googles Bard scrambling, but its early launch also garnered millions of users and generated vast amounts of data for OpenAI to push out GPT-4, an improved version of the bot, only months later.

Another example is Grammarly, whose finessing of its writing assistance system with the help of user feedback showcases the power of continuous A.I. improvement and adaptation, particularly in the complex and context-sensitive realm of languages.

Similarly, Apodigi, a frontrunner in the digitalization of the pharmacy business, launched in June 2020 an A.I.-assisted pharmacy app that can be described as learning on the job. Called Treet, the app proposes medication based on doctors prescriptions which a pharmacist then reviews and tweaks. The pharmacists responses coalesce into a stream of continuous feedback which finetunes the algorithm and contributes to better recommendations that address the complexities of each patients needs and preferences.

By comparison, IBM developed and tested Watson Health extensively in the laboratory and pushed out the diagnostic tool to market without incorporating continuous learning from real-world data. This traditional build-test-deploy process proved inadequate for training A.I.

Keep it safe

Safety mechanisms that protect consumers and safeguard reputations are essential in A.I. development. For example, Tesla runs new versions of its self-driving software in the background while a human drives the car. Decisions made by the software, say turning the steering wheel, are compared to the drivers. Any significant deviation or unusual decisions are analyzed, and the A.I. is retrained if required. Simulator environments such as AILiveSIM allow for full-scale AI systems to be safely and comprehensively tested before their deployment in the real world.

A.I. developed for creative applications arguably needs stronger guardrails. Analogous to children mixing with bad company and learning undesirable habits, A.I. could be exposed to training data that are riddled with biases and discriminatory content. 

To preempt this, OpenAI, for one, employs an approach called adversarial training to train its A.I. model to not be fooled by rogue inputs from attackers. This method involves exposing chatbots to adversarial content that threatens to overcome the bots standard constraints, enabling it to recognize rogue content and avoid falling for them in the future.

Capture behavior

In the ideal A.I. development cycle, developers log all user reactions and behavior to feed further development of the algorithm without questioning the accuracy or value of a recommendation or prediction. The Netflix A.I. content recommender, for example, simply notes whether a user watches the recommended content and the viewing duration. The algorithm learns from each response to make a better recommendation next time.

The developers of Watson Health could have achieved better outcomes if they had subscribed to this principle. Rather than programming the algorithm to solicit doctors evaluation of the A.I.-generated recommendation, they could have trained the system to simply record doctors prescriptions. Integrating Watson Health into patient information systems would also have immersed it in a feedback loop for continuous training based on actual cases and patient outcomes.

User feedback provides excellent training data for vertical applications with a specific focus.

But rather than relying on humans to label data, developers should think of ways to automate the process. For example, connecting a vehicles front camera feed with the steering wheel can automatically create labels for winding roads and feed into AI models learning to drive a car on complicated routes.

In fact, developers should deploy many automated data collectors and design explicit feedback loops for learning at scale. In the above example of driving assistance development, many vehicles can cover a wider variety of situations than just a few. A vehicle cutting in front of a Tesla triggers a video upload from the previous few seconds preceding the event. The system feeds the footage into Teslas deep neural network that learns the various signals, such as a gradual movement towards the lane divider, that predict the cut-in and take appropriate action like slowing down. In contrast, traditional car companies are often mired in a fixed mindset, developing and deploying driving assistance software with little automated feedback collection or data updating.

Continuous learning

Just as children do not stay in kindergarten forever; the training methodology for A.I. should be continuously upgraded. But too often, A.I. developers focus on the latest developments in A.I. algorithms and individual use cases rather than engineering the system to cover a large number of use cases and data streams.

Going one step further, companies can develop a simulation environment that generates synthetic data and allows for faster development cycles. For example, Tesla captures data from its fleet of cars to feed a simulator that simulates complex traffic environments, resulting in new synthetic training data.

Tero Ojanpera, Ph.D., has been a professor of practice on intelligent platforms at Aalto University since 2021. He is also co-founder and executive chairman of Silo AI, collaborating with numerous leading global corporations. Previously, he was Nokias CTO, chief strategy officer, and head of research. Treet is a former customer of Silo AI. AILiveSIM is a technology partner of Silo AI.

Timo Vuori, Ph.D., has been a professor of strategic management at Aalto University since 2013 and a visiting scholar at INSEAD from 2013-2015.

Quy Nguyen Huy, Ph.D., has been a professor of strategy at INSEAD since 1998 and chair of the schools Strategy department from 2010 to 2012. He is known for his pioneering work linking social-emotional and temporal factors to the organizational processes of strategic change and innovation.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

More must-read commentary published by Fortune: