Zim News Blog -NEWS NETWORK

Vice President Dr Constantino Chiwenga yesterday attended the inauguration of Tanzanian President Samia Suluhu Hassan following her overwhelming election victory. She won nearly 98 percent of the vote. In his remarks, VP Chiwenga, who represented President Mnangagwa at the ceremony, strongly criticised the violence that followed the Tanzanian elections, calling it outdated and unacceptable in

The AI spending spree continues apace with no end in sight. In earnings calls Wednesday, Meta, Google and Microsoft all said they would continue to hike their capital expenditures to build out AI data centers, on top of the $400 billion that major tech companies are on track to spend on AI infrastructure this year, according to Morgan Stanley estimates.

But so far, return on investment for AI use cases has been weak. A recent MIT study found that 95% of generative AI pilots at companies have failed to develop beyond the proof-of-concept stage. Leading AI labs like OpenAI are still having trouble with getting their AI to do ‘grunt work’ tasks of entry-level investment bankers. Generative AI seems to be heading towards its “trough of disillusionment” moment.

Chatbots like ChatGPT and Gemini can draft a perfect sonnet in a few seconds. They can code and write movie scripts. So why is AI still so far behind in actually being able to take over white collar work?

The answer: training data, specifically specialized real world workflows. Turns out that while there is an abundance of publicly available data for how to generate language or code, there is significantly less for how to manage an IPO or merger. That data is largely private.

For writing sonnets, models have been trained on an entire internet’s worth of text, which includes immense amounts of poetry and literature. This is data that AI companies were able to obtain for free by scraping the entirety of the internet.

“Large language models do well when you collect a lot of data, and we don’t have nearly as much data for real-world tasks.”Robert Nishihara, co-founder of Anyscale

On the other hand, for the real-world workflows that define an entry-level investment banker’s day, even things like specialized data entry and spreadsheet manipulation, there is almost no easily-scrapable training data.

“Large language models do well when you collect a lot of data, and we don’t have nearly as much data for real-world tasks, meaning the AI hasn’t seen the examples it needs to master these specific skillsets,” said Robert Nishihara, co-founder of Anyscale, a company that provides AI software infrastructure.

In real-world, multi-step tasks, generative AI’s inherent lack of control is a critical flaw. Because the AI produces different responses each time and is prone to hallucination, even small errors quickly compound across multiple steps, causing the entire workflow to rapidly go off the rails.

Error reduction is why AI labs are doubling down on domain-specific data collection with experts, because each domain has its own set of corner cases that needs to be accounted for, said Lake Dai, founder of Sancus Ventures.

It’s becoming a big industry: Surge AI, a company whose entire business is providing AI labs with human-generated training data, reached $1 billion in recurring revenue last year. Experts can make over $100 an hour to generate the data to automate their own jobs. That data can be people doing their office jobs for hours at a time, said Paco Guzmán, head of research at Handshake, a training data provider. For example, this could be an investment banker formatting a presentation in the right way, or a doctor entering patient notes into a health record system.

Experts can make over $100 an hour to generate the data to automate their own jobs.

“There’s a huge demand for this type of data because AI model makers want to be a partner for every single professional and help them increase productivity, so once they’re done with the financial domain, there is medical, recruiting, and an endless amount of other domains,” he said.

But even if AI labs manage to collect thousands, even millions of examples of white collar work, will AIs be able to wholesale do our jobs for us? Not necessarily, because current AI models still can’t learn like humans can, said Nishihara.

“To do a job, you need to be able to learn on the job,” he said. “Humans can learn on the job and learn from mistakes and only one example, but today’s AI models can’t do that,” he said. “We won’t fully replace white collar work until they can do that.” – Forbes

The AI spending spree continues apace with no end in sight. In earnings calls Wednesday, Meta, Google and Microsoft all said they would continue to hike their capital expenditures to build out AI data centers, on top of the $400 billion that major tech companies are on track to spend on AI infrastructure this year, according to Morgan Stanley estimates.

But so far, return on investment for AI use cases has been weak. A recent MIT study found that 95% of generative AI pilots at companies have failed to develop beyond the proof-of-concept stage. Leading AI labs like OpenAI are still having trouble with getting their AI to do ‘grunt work’ tasks of entry-level investment bankers. Generative AI seems to be heading towards its “trough of disillusionment” moment.

Chatbots like ChatGPT and Gemini can draft a perfect sonnet in a few seconds. They can code and write movie scripts. So why is AI still so far behind in actually being able to take over white collar work?

The answer: training data, specifically specialized real world workflows. Turns out that while there is an abundance of publicly available data for how to generate language or code, there is significantly less for how to manage an IPO or merger. That data is largely private.

For writing sonnets, models have been trained on an entire internet’s worth of text, which includes immense amounts of poetry and literature. This is data that AI companies were able to obtain for free by scraping the entirety of the internet.

“Large language models do well when you collect a lot of data, and we don’t have nearly as much data for real-world tasks.”Robert Nishihara, co-founder of Anyscale

On the other hand, for the real-world workflows that define an entry-level investment banker’s day, even things like specialized data entry and spreadsheet manipulation, there is almost no easily-scrapable training data.

“Large language models do well when you collect a lot of data, and we don’t have nearly as much data for real-world tasks, meaning the AI hasn’t seen the examples it needs to master these specific skillsets,” said Robert Nishihara, co-founder of Anyscale, a company that provides AI software infrastructure.

In real-world, multi-step tasks, generative AI’s inherent lack of control is a critical flaw. Because the AI produces different responses each time and is prone to hallucination, even small errors quickly compound across multiple steps, causing the entire workflow to rapidly go off the rails.

Error reduction is why AI labs are doubling down on domain-specific data collection with experts, because each domain has its own set of corner cases that needs to be accounted for, said Lake Dai, founder of Sancus Ventures.

It’s becoming a big industry: Surge AI, a company whose entire business is providing AI labs with human-generated training data, reached $1 billion in recurring revenue last year. Experts can make over $100 an hour to generate the data to automate their own jobs. That data can be people doing their office jobs for hours at a time, said Paco Guzmán, head of research at Handshake, a training data provider. For example, this could be an investment banker formatting a presentation in the right way, or a doctor entering patient notes into a health record system.

Experts can make over $100 an hour to generate the data to automate their own jobs.

“There’s a huge demand for this type of data because AI model makers want to be a partner for every single professional and help them increase productivity, so once they’re done with the financial domain, there is medical, recruiting, and an endless amount of other domains,” he said.

But even if AI labs manage to collect thousands, even millions of examples of white collar work, will AIs be able to wholesale do our jobs for us? Not necessarily, because current AI models still can’t learn like humans can, said Nishihara.

“To do a job, you need to be able to learn on the job,” he said. “Humans can learn on the job and learn from mistakes and only one example, but today’s AI models can’t do that,” he said. “We won’t fully replace white collar work until they can do that.” – Forbes

The Zimbabwe Anti-Corruption Commission has recovered and forfeited assets worth over US$39 million since 2019, in addition to referring corruption cases valued at over US$217 million to the National Prosecuting Authority of Zimbabwe. This represents a major milestone for the commission as it prepares to launch its 2026–2030 National Anti-Corruption Strategy (NACS2). It also follows an

The Commercial Farmers union (CFU) has commended the Government over ongoing efforts to improve the business environment through initiatives to reduce the cost of doing business in the agricultural sector.

Mr Liam Philp, the president of CFU, which mainly represents large-scale white commercial farmers, said this at the Horticulture Development Council (HDC) investor forum held on Thursday last week, whose proceedings included the Horticulture Investors Pitch to the Zimbabwean Economy.

In September, the Government started implementing bold measures to eliminate the bureaucratic obstacles that have long hindered farmers and the agriculture sector.

This includes cutting numerous fees and simplifying regulatory compliance requirements as part of the first phase of its ease of doing business and regulatory reform programme.

It is only the beginning of extensive business reforms cutting across key sectors, including retail, energy, transport, manufacturing, construction and tourism.

Agriculture has been chosen as the first testing ground for these reforms, with livestock, dairy and stockfeed producers now enjoying dramatic reductions in regulatory fees and permits.

Mr Philp indicated that the measures the Government is taking would stimulate increased productivity, enhance competitiveness and attract both domestic and foreign investment into agriculture.

The union noted that challenges such as high input costs, energy shortages and limited access to finance have been constraining growth in the sector.

However, policy consistency, investment in infrastructure and engagement with private players are beginning to yield positive results.

“I want to take this opportunity to thank the head of state and the Government of Zimbabwe for all the efforts, which they are putting into the business and the cost of doing business. There seems to be a strong commitment and tremendous engagement.

“We have seen the first draft of policy reforms come out for the livestock sector and it was very encouraging. There is a lot of work happening in terms of support. We recently submitted recommendations because there is a lot of reform that needs to happen in agriculture,” said Mr Philp.

CFU added that continued collaboration between the public and private sectors will be critical to sustain momentum and ensure that the benefits of reduced operating costs translate into improved productivity and profitability for farmers across the country.

This was affirmed by development finance institutions (DFIs) that have acknowledged the Government’s commitment to the ease of doing business reforms as one of the major reasons they are considering giving Zimbabwe another chance in terms of co-operation.

The DFIs cited that Zimbabwe is one of the best countries to support in terms of horticulture financing and production, given the country’s unique, mild climate, fertile soils and reliable sunlight, which allow all year-round production across multiple agro-ecological zones.

Growing interest in local horticulture comes as the HDC indicated that it aims to transform Zimbabwe’s horticultural sector into a US$2,5 billion industry by 2030, driven by sustainable investment, value addition and strategic partnerships.

Zimbabwe’s agriculture sector remains one of the key anchors of economic recovery, contributing significantly to gross domestic product export earnings.

Recent Government reforms, including investment incentives, currency stabilisation measures, and improved land tenure systems, were also cited as steps that could boost investor confidence. – Herald